Short-term Clinical Outcomes and Implementation Insights: A Hybrid Cluster Randomized Controlled Trial of an “Add-On Online Mental Health Training” for Primary Care Doctors

Abstract

Background:

Training primary care doctors (PCDs) enables them to provide timely and effective treatment of psychiatric disorders in community settings. However, its impact on clinical outcomes remains relatively unexplored.

Methods:

This cluster randomized controlled trial compared “Training As Usual” [TAU; 2 days of in-person, lecture-based training; control group (CG)] with TAU plus an 18.5-hour Online Mental Health Training (OMHT; study group [SG]) for PCDs. Clinical outcomes (n = 41; SG = 24, CG = 17) were assessed at baseline and after eight weeks using standardized scales (K10, EQ-5D-5L, IDEAS, CGI-S, BARS) across common (CMDs), severe (SMDs), and substance use disorders (SUDs, including alcohol and tobacco). Both groups completed questionnaires evaluating implementation aspects such as acceptability, adoption, appropriateness, feasibility, fidelity, and provided feedback on training relevance, changes in practice, and operational barriers.

Results:

Overall (all disorders put together), significant improvements in subjective perception of health and disease severity were found in both study (EQ-VAS, p < .01; CGI-S, p = .03) and CG (EQ-VAS, p < .01; CGI-S, p = .05), and a reduction in distress score (K10; p = .01) in SG alone. Domain-wise analysis showed significant improvements in K10 (p = .02), EQ-VAS (p = .04), and CGI-S (p = .04) scores only in SG for CMDs. The same was not true for SMDs or SUDs. PCDs reported high acceptability, adoption, and appropriateness (over 90%) for integrating mental health into primary care. However, feasibility (71.4–86.4% citing support systems) and fidelity (only 9% of consultations had psychiatric diagnoses, and just 25% of those received psychotropic prescriptions) were lower. Significant barriers included staff and medication shortages, stigma, and limited mental health awareness.

Conclusions:

Training PCDs has the potential to yield significant short-term clinical benefits for individuals with common psychiatric disorders. Supplementary training may provide additional clinical advantages, particularly for CMDs. PCDs identified multiple challenges in incorporating mental health services into primary care practice.

Trial Registration:

The Clinical Trial Registry of India (Registration No. CTRI/CTRI/2024/02/062906.

Keywords

Patient outcomes clinical outcomes primary care doctors primary mental health care interventions

Key Messages:

Training enhances primary care doctors’ skills to deliver effective primary mental health care. Add-on online training can result in additional clinical benefits to patients with common mental disorders.

Despite high acceptability, adoption, and appropriateness, implementation findings revealed challenges in feasibility and fidelity, highlighting the need for structural and system-level support to translate training into actual clinical care delivery.

Approximately 11% of the Indian population experiences some form of mental illness, yet only a fraction of them receive adequate treatment.¹ Training primary care doctors (PCDs) in mental health not only makes it possible to provide timely and effective treatment but also helps to screen for mental health issues early, often identifying concerns before they escalate into more severe stages that require specialized care.²

Research shows that PCDs can diagnose and treat mental health conditions, but many struggle with translation into real-world clinical practice. For example, a study,³ found that in India, PCDs identified only 20%–40% of mental health cases, revealing a significant gap in care.

Innovative training programs for PCDs have been implemented, with their effectiveness evaluated using tools such as pre- and post-Knowledge, Attitude, and Practice (KAP) questionnaires, the Primary Care Psychiatry Quotient (PCPQ), Translational Quotient (TQ), accreditation, etc.^4–9 Further, reductions in symptom severity and disability have been observed when care is delivered by teams dedicated to specific research projects.^10,11

However, there is a dearth of studies evaluating patient-level outcomes involving PCDs working in public healthcare delivery systems. This study aimed to assess secondary outcomes from a larger hybrid cluster randomized controlled trial evaluating the integration of mental health services into primary care. Specifically, it sought to: (a) assess short-term clinical outcomes among patients managed by two groups of PCDs—one receiving “training as usual” and the other receiving standard training plus add-on mental health training; (b) examine implementation outcomes on integrating mental health services into primary care including acceptability, adoption, appropriateness, feasibility, and fidelity; and (c) identify key barriers and facilitators influencing mental health service delivery at the primary care level.

Methods

This study focuses on secondary outcomes of an effectiveness-implementation-hybrid-cluster Randomized Control Trial (RCT), done as part of the impact and outcome evaluation of a “Multistate digitally driven mental health capacity building program for strengthening primary mental healthcare in India” (Shah H, Sabhahit GG, Patley R et al., 2025, titled “A Pan India Digitally Driven Capacity Building Program to strengthen Primary Mental healthcare: Summary of its Implementation and Performance evaluation”; accepted for publication). This study adopted an effectiveness-implementation hybrid cluster randomized controlled design for two key reasons. First, clustering at the taluk level ensured that doctors within a given taluk were allocated to the same training condition. This approach reduced the likelihood of cross-arm influence. It mirrored how training programs are typically implemented in real-world health systems, where interventions are rolled out at the administrative unit level rather than at the level of individual doctors. Second, the hybrid design enabled simultaneous evaluation of both clinical effectiveness (diagnostic/treatment concordance, patient outcomes) and implementation outcomes (acceptability, adoption, appropriateness, feasibility, fidelity), providing evidence not only on the effectiveness of the intervention but also on the contextual factors that influence its integration into routine practice. The study was conducted in the Tumkur district of Karnataka State. The study was approved by the Institutional Ethics Committee (IEC) (Approval No. NIMHANS/43rd IEC(BEH.SC.DIV) 2023, dated 8 December 2023, NIMHANS/EC(BH.SC.DIV.) MEETING/2024 dated 25 October 2024, and No. NIMHANS/EC(BEH.SC.DIV.) MEETING/2025

dated 1 July 2025) and registered with the Clinical Trial Registry of India (Registration No. CTRI/CTRI/2024/02/062906).

Participants were PCDs working across selected Taluks in the district. Baseline assessments were conducted from May to June 2024, and follow-up assessments were conducted from July to August 2024. A detailed account of the methodology of this RCT is discussed in the article titled “An Effectiveness-implementation Hybrid Cluster Randomized Controlled Trial to evaluate add-on online mental health training for Primary Care Doctors in influencing their management of commonly prevalent mental health disorders: Description of the methodology” in this supplement (Sabhahit GG, Das N, Ramachandraiah R et al., 2025, titled An Effectiveness-implementation Hybrid Cluster Randomized Controlled Trial to evaluate add-on online mental health training (OMHT) for PCDs in influencing their management of commonly prevalent mental health disorders: Description of the methodology).

To summarize, the trial aimed to compare two methods of training PCDs: Digitally driven OMHT as an add-on to Training As Usual (OMHT + TAU; study group [SG]) compared to “Training As Usual” (TAU; control group [CG]). TAU is the routine training of PCDs through the District Mental Health Program. Usually, TAU contains 2 days of in-person training delivered in an essentially didactic format. This was conducted at the district centers. TAU occurs every year, and all PCDs in the district were directed to attend this training. In OMHT, the training duration was initially 6 weeks, with one virtual online session per week, each lasting about 2 hours. Further revision sessions lasted 6.5 hours in total, comprising 12 sessions averaging 30-40 minutes each. The total training duration, including revision classes, was 18.5 hours. This was in addition to TAU. PCDs were trained on substance use disorders (SUDs), which include Tobacco Addiction and Alcohol Use Disorders, Common Mental Disorders (CMDs) comprising Depression, Generalized Anxiety Disorder, Panic Disorder, Somatisation Disorder, Mixed disorder, and Severe Mental Disorders (SMD) encompassing all psychoses. PCDs were trained according to the manual “Clinical Schedules of Primary Care Psychiatry version 2.4,” which is designed primarily to identify and treat (first-line management) uncomplicated psychiatric disorders that commonly present to primary care settings. Any complicated presentations (e.g., alcohol withdrawal delirium, violence, suicidality, episodic psychosis, etc.) would be referred to higher treatment centres.¹²

Supportive handholding was done through Collaborative Video Consultations (CVCs) for 3 months. A separate team of assessors, comprising psychiatrists, psychiatric social workers, and psychologists (two at a time), conducted outcome evaluations at both baseline and follow-up (8 weeks after baseline). Although different raters were involved, all assessors were trained in the use of the standardized instruments, and the same team conducted assessments across both intervention and control arms. Due to time and resource constraints, follow-up assessments were conducted by telephone. During the baseline assessments, though other mental health professionals administered the scales, diagnosis and treatment decisions were made solely by psychiatrists. The assessors remained blinded to the training and randomization status.

The study included 13 doctors in the SG and 13 in the CG (Table 1). At baseline, 578 patients were recruited, of whom 235 were identified as having a psychiatric diagnosis. These 235 patients were approached for follow-up evaluation. However, 95 were lost to follow-up, as mentioned in Figure 1. Follow-up assessments were completed for 140 patients (61 in the SG and 79 in the CG). Written informed consent was obtained from both PCDs and patients/caregivers. Primary outcomes were diagnostic, treatment, and combined (diagnostic and treatment combined) concordance of PCDs with the assessors (i.e., psychiatrists). (Jayashri S, Sabbella C, Singh S et al., 2025, titled “Diagnostic and treatment concordance among primary care doctors delivering mental health care: Results from An Effectiveness-implementation Hybrid Cluster Randomized Controlled Trial to compare two methods of training”). Secondary outcomes (of interest to this article) included (a) clinical outcomes measured through standardized scales and (b) the implementation questionnaire.

Table 1.

Sociodemographic Profile of PCDs in the Study and Control Groups.

	Study Group [n (%)] (N = 13)	Control Group [n (%)] (N = 13)	p Value
Age (mean)	38.77 (9.58)	41.85 (9.31)	.41
Gender Males Females	6 (46.2%) 7 (53.8%)	10 (76.9) 3 (23.1)	.11
Years since completion of MBBS (mean)	15.23 (8.50)	18.08 (9.02)	.42
Work experience in years (median)	12 (7,19)	15 (7, 21)	.52
Psychiatric training during MBBS Yes No	10 (76.9) 3 (23.1)	10 (76.9) 3 (23.1)	1.00
If yes, duration in MBBS in days (median)	15 (0.5, 22.5)	15 (1.5, 15)	.71
Psychiatric training during internship Yes No	12 (92.3) 1 (7.7)	10 (76.9) 3 (23.1)	.59
If yes, duration in internship in days (median)	15 (15,15)	15 (3.5, 15)	.31
Psychiatric training in the last one year* Yes No	7 (53.8) 6 (46.2)	8 (61.5) 5 (38.5)	.43
DMHP training status Completed Not completed	9 (69.2) 4 (30.8)	11 (84.6) 2 (15.4)	.64
No. of sessions attended (mean)	9.38 (3.20)	Not applicable	NA

*Includes any psychiatric training in the past one year, such as DMHP training or online modules related to psychiatry.

Figure 1.

Flow Chart Showing the Recruitment and Flow of Participants in the RCT.

Among the 140 follow-up patients, diagnostic concordance between the psychiatrist and PCD was found in 82 patients (29 in the SG and 53 in the CG). Among these, treatment concordance was observed in only 41 patients (24 in the SG and 17 in the CG) (Figure 1). Both diagnostic and treatment concordance were verified manually by the psychiatrist during the analysis. This article focuses on the short-term outcomes for these 41 patients.

The following tools were used at baseline as well as at follow-up:

Kessler Psychological Distress Scale (K10):

The K-10 is a self-administered questionnaire consisting of 10 items, each with a five-level response scale that assesses emotional distress, based on questions regarding an individual’s emotional state over the past four weeks. The scale has been validated for the Indian population with good reliability and validity.^13–15

EuroQol-5 Dimension (EQ-5D-5L): The EQ-5D-5L is a widely used health-related quality of life comprising two components. The descriptive system assesses health across five dimensions—mobility, self-care, usual activities, pain/discomfort, and anxiety/depression—each with five response levels (no problems, slight problems, moderate problems, severe problems, extreme problems/unable to). Responses generate a 5-digit code representing the health state, which can be converted into a single summary index value. This index reflects the overall quality of the health state based on societal preference weights derived from a representative sample of the general population. The scoring algorithm applies these weights to each level within each dimension and subtracts the total from 1 (full health, health state 11111). For the present study, the standard EQ-5D-5L value set for the Indian population was used. The second component of the questionnaire is a visual analog scale (VAS), on which respondents rate their current overall health from 0 (worst imaginable health) to 100 (best imaginable health).^16,17

Indian Disability Evaluation and Assessment Scale (IDEAS): “IDEAS evaluates disability in self-care, social interactions, communication, and work using a 5-point scale. It also considers the overall disability score and the duration of the illness, categorizing levels of disability.” IDEAS Scale has good internal consistency (Cronbach’s alpha of 0.72–0.89), construct validity, and strong concurrent validity with WHODAS 2.0.^18,19

Clinical Global Impression (CGI): The CGI is a 3-item observer-rated scale. In this study, we focused on the CGI-S component, which measures the severity of illness on a 7-point Likert scale: 1 (normal), 2 (borderline mentally ill), 3 (mildly ill), 4 (moderately ill), 5 (markedly ill), 6 (severely ill), and 7 (extremely ill). Higher scores indicated greater symptom severity and functional impairment. The CGI Scale has demonstrated strong interrater reliability and validity across clinical studies.²⁰

Brief Addiction Rating Scale (BARS): The BARS is an interview-based tool that assesses health and life issues in individuals with SUDs. The 10-item scale, administered over the past month, uses a 7-point scale (0–6) to rate the presence of new and ongoing problems. The ratings under each construct should be based on all sources of information (e.g., self-report, collateral reports, documents, medical examination records). The 10 items include dyscontrol of substance use, family role dysfunction, occupational dysfunction, social problems, legal problems, financial problems, general ill-health, psychiatric illness, neuropsychiatric syndromes, and sexual issues. High interrater reliability (0.9) was observed across all items of the scale.²¹

Implementation Outcomes: Quantitative and Qualitative Assessment

This component assessed PCDs’ opinions about integrating mental health care into primary care using implementation science measures. A semi-structured questionnaire was developed. A couple of group discussions were carried out among the project team, which included nine psychiatrists and three faculty supervisors with extensive research experience. Discussions focused on creating a semi-structured questionnaire (Table 4) to gather PCDs’ perceptions through key implementation outcome variables: “Acceptability,” “Adoption,” “Appropriateness,” “Feasibility,” and “Fidelity” based on the Conceptual Framework for Implementation Outcomes, developed by Proctor and colleagues (2011).²² Operational definitions of these domains were provided in Supplementary document 1. Questions were rated on a 4-point Likert scale (0 to 3), where 0 indicated “Not at all,” 1 “Very little,” 2 “Somewhat,” and 3 “Very much.” An open-ended descriptive questionnaire was also included to capture doctors’ experiences with the training, changes in clinical practice, and perceived barriers to implementation. The implementation questionnaire was distributed to all 26 doctors of both SG and CG; however, responses were received from only 22 [n = 22, SG doctors (n = 11), CG doctors (n = 11)].

Statistical Analysis

Normality was assessed using the Kolmogorov–Smirnov test. Continuous variables were summarized using mean/median, whichever was appropriate. A significant difference between pre- and post-scores was measured using the Wilcoxon signed-rank test. Statistical significance was defined as a p < .05 (α < 0.05). All statistical analyses were conducted using the licensed IBM SPSS software, version 29.0.

For the Implementation questionnaire, descriptive statistics (frequencies, percentages, medians, IQRs) were used to summarize responses, and comparisons were conducted between SG and CG. Likert scale responses, which typically range from “Very much” to “Very little,” were treated as ordinal data. Responses were assigned numerical values (e.g., 1 = “Not at all” & “Very little,” 2 = “Somewhat” & “Very much”) to facilitate further analysis. We conducted a thematic analysis of the qualitative responses to the descriptive (open-ended) questions using the six-phase approach outlined by Braun and Clarke (2006).²³ This involved familiarization with the data, generating initial codes, identifying and reviewing themes, defining and naming themes, and producing the final report.

Results

Table 1 presents the sociodemographic profile of PCDs. Both groups were comparable in terms of age, gender, years since MBBS completion, and work experience. Both groups had comparable exposure to psychiatry during their undergraduate medical education and internship. Recent psychiatric training rates were also similar. On average, SG PCDs had attended 9.38 add-on OMHT sessions.

Table 2 presents the clinical outcomes of SG and CG across all disorders. In SG, significant improvements were observed in psychological distress (K10; p = .014), subjective health perception (EQ-VAS; p = .009), and clinical severity (CGI-S; p = .026). In the CG, only EQ-VAS (p = .007) and CGI-S (p = .047) showed statistically significant improvement. No significant differences were noted in the IDEAS, EQ-5D-5L Index in either group. Including effect sizes alongside p values provides a clearer picture of clinical significance. For all disorders combined, large effect sizes were observed for the reduction of distress (K10) in the SG and for quality of life (EQ-VAS) in both the study and CG. A medium effect size was observed for illness severity (CGI-S) in both groups.

Table 2.

Clinical Outcomes for all Disorders Put Together.

Scales	Study Group						Control Group
	Pre Value		Post Value		p Value	Effect Size	Pre Value		Post Value		p Value	Effect Size
	n	Median (IQR)	n	Median (IQR)	p Value	Effect Size	n	Median (IQR)	n	Median (IQR)	p Value	Effect Size
K10^*	23	19 (11, 22)	22	12 (10, 16)	.014	–0.522	17	13 (10, 19)	17	11 (10, 20)	.767	–0.072
EQ-VAS^@	24	60 (50, 70)	22	70 (55, 80)	.009	–0.561	17	50 (30, 65)	17	60 (45, 70)	.007	–0.655
EQ-5D index value^$	24	0.83 (0.52, 0.88)	23	0.80 (0.73, 0.97)	.156	–0.296	17	0.72 (0.62, 0.98)	17	0.72 (0.45,0.98)	.279	–0.263
IDEAS^	24	4 (2, 6.75)	23	3 (2, 5)	.478	–0.148	17	5 (4, 8)	17	5 (4, 7)	.500	–0.163
CGI-S^&	24	4 (4, 5)	24	3 (2, 5)	.026	–0.454	17	4 (3, 4)	17	3 (3, 4)	.047	–0.481
BARS^!	3	15 (12)	3	18 (4)	.285	0.617	2	39 (39, 39)	2	34 (30)	.317	0.707

Notes: p Value using the Wilcoxon signed-rank test.; Bold values indicate that p < .05 is considered statistically significant.; ^*K10 (Kessler Psychological Distress Scale),; ^@EQ-VAS (Euro QOL Visual Analog Scale),; ^$EQ-5D Index value (Euro QOL 5-dimensional questionnaire index value),; ^{^}IDEAS (Indian Disability Evaluation and Assessment Scale),; ^&CGI-S (Clinical Global Impression-Severity) scales were used for all disorders (Common Mental Disorders, Severe Mental Disorders, Substance Use Disorders).

Table 3 focuses on sub-groups with CMDs, SMDs, and SUDs. For CMDs in SG, significant improvements were observed in psychological distress (K10, p = .02), subjective health perception (EQ-VAS, p = .04), and clinical severity (CGI-S, p = .04) from baseline to follow-up. Conversely, no statistically significant improvements were noted in CG for any measure. For common mental disorders, large effect sizes were observed for psychological distress (K10), quality of life (EQ-VAS), and clinical severity (CGI-S), supporting meaningful improvements even with modest sample sizes. For SMDs and SUDs, no statistically significant improvements were observed in either group.

Due to limited numbers, BARS^! (Brief Addiction Rating Scale) findings for Alcohol Use Disorder were deemed non-interpretable.

Table 3.

Clinical Outcomes for Common Mental Disorders, Severe Mental Disorders, and Substance Use Disorders.

	Scales	Study Group						Control Group
		Pre Value		Post Value		p Value	Effect Size	Pre Value		Post Value		p Value	Effect Size
		n	Median (IQR)	n	Median (IQR)	p Value	Effect Size	n	Median (IQR)	n	Median (IQR)	p Value	Effect Size
Common mental disorders	K10^*	14	21.5 (13.75, 24.5)	14	12 (10, 21.25)	.02	–0.641	6	19.5 (17.25, 26.75)	6	20.5 (14.5, 28.75)	.85	0.349
	EQ- VAS^@	14	60 (58.75, 70)	14	72.5 (53.75, 82.5)	.04	–0.549	6	55 (28.75, 60)	6	55 (45, 62.5)	.11	0.044
	EQ-5D-5L index value^#	14	0.71 (0.36, 0.85)	14	0.79 (0.47, 0.92)	.13	–0.402	6	0.62 (0.35, 0.71)	6	0.45 (0.34, 0.58)	.25	0.102
	IDEAS^$	14	2.5 (2, 5.25)	14	2 (1.75, 4.75)	.60	–0.142	6	5 (3.5, 10.25)	6	5 (4.25, 8.75)	.66	0.267
	CGI-S^{^}	14	4 (3, 5)	14	3 (1, 4.25)	.04	–0.549	6	4 (3, 4)	6	4 (3, 4)	1.00	0.408
Severe mental disorders	K10	3	14 (10)	2	12 (10)	1.00	0.707	2	10.5 (10)	2	12 (11)	.32	0.224
	EQ- VAS	3	50 (30)	2	60 (40)	.18	–0.949	2	30 (20)	2	35 (30)	.32	0.224
	EQ-5D-5L index value	3	0.89 (0.58)	2	0.87 (0.77)	.66	0.463	2	0.81 (0.66)	2	0.77 (0.59)	.18	0.127
	IDEAS	3	7 (6)	2	5 (3)	.32	0.224	2	7.5 (7)	2	7.5 (7)	1.00	0.707
	CGI-S	3	5 (4)	3	3 (2)	.41	0.239	2	3.5 (3)	2	3.5 (3)	1.00	0.707
Substance use disorders	K10	7	11 (10, 26)	7	11 (10, 14)	.27	0.102	7	10 (10, 21)	7	10 (10, 26)	.59	–0.202
	EQ- VAS	7	70 (60, 85)	7	80 (70, 95)	.23	0.084	7	60 (50, 70)	7	70 (60, 75)	.06	–0.702
	EQ-5D-5L index value	7	0.87 (0.51, 1)	7	0.92 (0.78, 1)	.50	0.188	7	0.76 (0.61, 1)	7	0.94 (0.42, 1)	.50	–0.255
	IDEAS	7	4 (4, 4)	7	4 (2, 4)	.41	0.156	7	5 (4, 10)	7	5 (4, 7)	.66	–0.169
	CGI-S	7	4 (3, 5)	7	3 (1, 5)	.46	0.174	7	4 (3, 5)	7	4 (3, 4)	.16	–0.534
	BARS^&	3	15 (12)	3	18 (4)	.285	0.617	2	39 (39, 39)	2	34 (30)	.317	0.707

Notes: p Value using Wilcoxon signed-Rank test; Bold values indicate that p < .05 considered statistically significant.; ^*K10: Kessler Psychological Distress Scale,; ^@EQ-VAS: Euro QOL Visual Analog Scale,; ^#EQ-5D-5L Index value: Euro QOL 5-dimensional questionnaire index value,; ^$IDEAS: Indian Disability Evaluation and Assessment Scale,; ^{^}CGI-S: Clinical Global Impression-Severity. Due to the limited numbers,; ^&BARS (Brief Addiction rating scale) findings for Alcohol Use Disorders were deemed non-interpretable.

Patients with co-occurring diagnoses were represented in all relevant categories.

Table 4 summarizes outcomes of mental health integration into primary care by PCDs. For acceptability (A1–A4), 90.9%–100% of doctors approved the training. Adoption (B1–B2) figures show 95.5%–100% intended to begin identifying and treating mental health conditions post-training. Appropriateness (C1–C2) ratings were similarly high (95.5%–100%) for relevance to PHC patients. However, feasibility was lower: only 71.4%–86.4% found it practical to apply in routine workflows with existing resources. On average, doctors saw 47.5 patients daily; just four had psychiatric diagnoses, and only one began treatment, translating to 9% diagnosed and 25% of those prescribed psychotropic medication.

Table 4.

Perspectives on Implementation Factors About Integrating Mental Health into Primary Care by Primary Care Doctors.

Domains	Questions	Responses (1= “Not at all” & “Very little”) (2 = “Some what” & “Very much”)	All PCDs (n = 22)	PCDs		p Value
Domains	Questions		All PCDs (n = 22)	Study Group (n = 11)	Control Group (n = 11)	p Value
Acceptability	A1: Do you agree with integrating mental health training for primary care providers to diagnose and initiate first-line treatment for commonly prevalent mental illnesses as part of routine care?	1	0 (0)	0 (0)	0 (0)	No statistics are computed because A1 is a constant.
		2	22 (100)	11 (100)	11 (100)	No statistics are computed because A1 is a constant.
	A2. How comfortable are you with diagnosing and treating mental health conditions because of the training?	1	2 (9.1)	2 (18.2)	0 (0)	.476
		2	20 (90.9)	9 (81.8)	11 (100)	.476
	A3. Do you see any significant advantages to incorporating the training on mental health services into routine primary care?	1	2 (9.1)	1 (9.1)	1 (9.1)	1.000
		2	20 (90.9)	10 (90.9)	10 (90.9)	1.000
	A4. How credible do you think it is for trained primary care providers to diagnose and treat mental illnesses	1	1 (4.5)	1 (9.1)	0 (0)	1.000
		2	21 (95.5)	10 (90.9)	11 (100)	1.000
Adoption	B1. Following your training, how likely were you to start identifying persons with mental health problems in the community?	1	0 (0)	0 (0)	0 (0)	No statistics are computed because B1 is a constant.
		2	22 (100)	11 (100)	11 (100)	No statistics are computed because B1 is a constant.
	B2. Following your training, how likely were you to start treating identified mental health problems in the community?	1	1 (4.5)	1 (9.1)	0 (0)	1.000
		2	21 (95.5)	10 (90.9)	11 (100)	1.000
Appropriateness	C1. Is the training on identifying and treating uncomplicated mental health disorders appropriate for the patients who visit PHCs routinely?	1	0 (0)	0 (0)	0 (0)	No statistics are computed because C1 is a constant.
		2	22 (100)	11 (100)	11 (100)	No statistics are computed because C1 is a constant.
	C2. How relevant do you consider addressing mental health issues with patients who routinely visit PHCs for care as part of your training?	1	1 (4.5)	0 (0)	1 (9.1)	1.000
		2	21 (95.5)	11 (100)	10 (90.9)	1.000
Feasibility	D1. How practical do you believe it is to integrate the training on the identification and treatment of non-complicated psychiatric disorders into the routine workflow of PHCs?	1	3 (13.6)	0 (0)	3 (27.3)	.214
		2	19 (86.4)	11 (100)	8 (72.7)	.214
	D2. How well do the current resources at PHCs support the implementation of the mental health training?	1	6 (28.5)	4 (40)	2 (18.2)	.361
		2	15 (71.4)	6 (60)	9 (81.8)	.361
Fidelity	E1. In the preceding month, how many patients did you see in the outpatient department (OPD) on an average day?	Median (IQR)	47.5 (36.25, 76.25)	45 (40.80)	50 (10.75)	.766
	E2. Of the total patients seen last month, how many were diagnosed with one or more mental health disorders?	Median (IQR)	4 (1.75, 12.5)	4 (1.10)	4 (2.20)	.716
	E3. Of those diagnosed with psychiatric conditions last month, how many were started on treatment at the PHC?	Median (IQR)	1 (0, 2.75)	1 (0.5)	1 (0.2)	.973

Table 5 summarizes qualitative findings from the thematic analysis of PCDs’ experiences with mental health training and its integration into routine practice. Three major themes emerged: (a) Perceived relevance and structure of training—Doctors found the training comprehensive and relevant to PHC settings. It enhanced their initial confidence in handling mental health issues, though many expressed the need for longer, repeated sessions and preferred offline formats due to workflow constraints. (b) Changes in diagnostic and treatment practices post-training—Participants reported improved ability to identify CMDs and initiate treatment. However, there was hesitancy in prescribing medications due to concerns about dosage and side effects. Referral remained the preferred approach for complex or severe cases. (c) Structural and operational barriers—High outpatient load, shortage of staff and psychotropic medications, and stigma were identified as key challenges in sustaining mental health integration within PHCs.

Table 5.

Thematic Analysis of Primary Care Doctors’ Experiences with Mental Health Training, Practice, and Barriers.

Theme	Subtheme	Description	Illustrative Quotes
1. Perceived relevance and structure of mental health training	Training was informative and relevant	Training was described as practical, comprehensive, and relevant to daily PHC work.	“Very useful and practical”; “Good teaching and knowledge sharing”; “It was an eye-opener”
	Built initial confidence in mental health care	Participants reported increased comfort in identifying and addressing common mental disorders (CMDs).	“Now I am happy to start treatment”; “Now I am confident to identify the condition”
	Need for repeated or longer-duration sessions	Periodic and elaborate sessions were requested. Continued mentorship was deemed necessary.	“Training should be more elaborate and longer term”; “Training every 2-3 years is needed”; “Repeated training helps”; “Training and reorientation required periodically”
	Preference for offline, localized training	Offline training was preferred over online modalities due to workflow at PHCs.	“Offline training only up to our PHC is needed”; “Online programs don’t allow us to concentrate”
2. Changes in diagnostic and treatment practices post-training	Improved ability to identify mental health conditions	Participants noted they were able to identify CMDs and were able to initiate conversations with the patients and counsel them.	“CMD patients are now being identified”; “I give counseling with calm talk and interview”;
	Hesitancy in initiating pharmacological treatment	Some remained unsure about dosage, side effects, or lacked confidence in prescribing.	“Prescribing the right dosage was a trouble”; “Not confident in initiating the treatment”
	Referral for complex/severe cases	For SMDs or complex cases, referral to psychiatrists was the preferred strategy.	“Simple cases I treat… others I refer to psychiatrist”; “Treated a case of psychosis with help of DMHP psychiatrist”
	Perceived improvement in patient satisfaction	Patients reportedly appreciated local access and support.	“They are happy they no longer need to go to cities”; “Patients are feeling good as we identify and treat CMD”
3. Structural and operational barriers to integrating mental health into primary care	High patient load and limited continuity of care	Overcrowded OPDs have limited time for mental health assessments. Patient noncompliance and a lack of dedicated follow-up personnel further constrained continuity of care.	“Too crowded OPD”; “Cannot spare much time for each patient”; “Need 10 minutes per patient”; “Busy OPD and no manpower for follow-up”; “Patients are lost to follow-up”; “Poor compliance”; “Patients don’t return unless we call”
	Shortage of staff and medications	Non-availability of psychotropic medications, absence of a dedicated staff/pharmacist hindered regular maintenance of psychotropics.	“Drugs are not always available”; “Need full drug availability” “No staff”; “No pharmacist”
	Stigma and lack of mental health awareness	Societal stigma and lack of mental health awareness affected engagement, early identification, and adherence to care.	“Patients are hesitant to visit early”; “Need co-response from patient attendees”

Discussion

This study demonstrates that add-on OMHT to PCDs translates into better short-term clinical benefits, particularly for patients with CMDs. In the overall sample, significant improvements were observed in subjective health perception (EQ-VAS) and clinical severity (CGI-S) in both the SG and CG. In contrast, a substantial reduction in psychological distress (K10) was found only in the SG ( Table 2 and Figure 2 ). Subgroup analysis further revealed that among patients with CMDs, those in SG experienced statistically significant improvements in distress (K10), subjective health perception (EQ-VAS), and clinical severity (CGI-S) ( Table 3 and Figure 2 ). These findings suggest that while both training formats may offer benefits, supplementing routine training with focused, interactive online modules may enhance PCDs’ ability to recognize and manage CMDs more effectively in primary care settings. Our findings align with earlier research indicating that equipping PCDs with skills to identify and manage depression, anxiety, and somatization disorders can be particularly beneficial in resource-limited settings.^24,25

Figure 2.

Showing Box and Whisker Plots of Comparison of Scales (K10, EQ-VAS, CGI-S) for all Patients Put Together and for CMDs (Common Mental Disorders).

However, for SMDs, improvements in subjective health perception (EQ-VAS) were observed. Short follow-up duration may have limited the detection of meaningful change in disability and quality-of-life scores. Schiffler et al.²⁶ also emphasized that integrating mental health into primary healthcare frameworks can mitigate the progression of severe mental health impairments. Likewise, in SUDs, numerical improvements in subjective perceptions of health and quality of life were observed, aligning with prior studies that support the efficacy of brief interventions by primary care physicians for managing harmful alcohol consumption.²⁷ These findings suggest that interventions at the level of primary care for substance use show promising outcomes in low- and middle-income countries.²⁸

Regarding implementation issues, nearly all doctors rated the training as highly acceptable, appropriate, and adoptable, suggesting strong attitudinal readiness to integrate mental health into their practice. However, lower scores in feasibility and fidelity reflect practical challenges in the real-world application of mental health care at the PHC level. These gaps suggest that increased knowledge did not fully translate into increased service delivery. Thematic analysis further elaborated the nature of these challenges (Table 5). Practical barriers frequently encountered were high outpatient loads, inconsistent drug availability, and the absence of dedicated staff to medication dispensing or follow-up, which impeded the continuity of care. Additionally, stigma and low patient awareness were identified as key factors that reduced service uptake and follow-up engagement.

The findings align with earlier implementation studies showing that capacity building must be paired with health system strengthening, such as the continuous supply of essential psychotropic medications, designated staff, and ongoing professional support, to achieve meaningful and sustainable integration of mental health into primary care.²⁹ Policy-level actions to improve feasibility can include continued mentoring of PCDs by psychiatrists (e.g., those working in District Mental Health Program, Medical Colleges, Digital Academies, etc.), adequate staff recruitment, and allocating existing staff, such as Community Health Officers (CHOs), for brief counselling and follow-up to get sustainable gains and maintain treatment continuity. State-level Academies on the lines of NIMHANS Digital Academy,³⁰ can act as continuous specialist resources for supporting PCDs on an ongoing basis. Tele MANAS Mentoring Institutes can double up as digital academies too (Sabbella C, Shah H, Hegde PR et al., 2025; titled Redefining Access to Mental Health Care through Sustained Tele-mentoring: A report of the instant CVCs of tele-psychiatrists with PCDs).

Strengths

The use of validated scales ensured robust measurement of outcomes. The hybrid cluster randomized design allowed for evaluation of both clinical effectiveness and contextual determinants that shape sustainability and scale-up. Including regular employees of public health systems enhanced the generalizability of the findings.

Limitations

A substantial attrition rate constrained sample size, potentially diminishing the statistical power to detect significant effects, particularly in subgroup analyses. Disorder-specific scales might have given more detailed inputs into the outcomes of these disorders. Scales used in this study, such as IDEAS and EQ-5D Index values, capture outcomes that may take longer to reflect meaningful change. Different assessors at baseline and follow-up may have affected interrater reliability. Our smaller sample sizes (13 doctors per arm) may have affected generalizability.

Future Recommendations

Future studies should consider a more extended follow-up period, enabling more accurate evaluation of the sustained effects of interventions and their impact on long-term outcomes. To obtain more detailed and nuanced information on the specific outcomes of each disorder, future studies could incorporate disorder-specific scales.

Conclusions

These findings demonstrate that structured training interventions can significantly improve the competencies of PCDs to identify and manage mental health disorders. Findings from the implementation outcomes highlight the need for dedicated staff, ongoing support, and availability of psychotropics to ensure effective and sustained integration of psychiatric care into routine PHC services.

Supplemental Material

Supplemental material for this article available online.

Supplemental Material

Supplemental material for this article is available online.

Footnotes

Acknowledgements

Same as the “ Introduction ” article of this issue (Indian J Psychol Med. 2026;48(1 suppl)).

Declaration of Conflicting Interests

The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Author Channaveerachari Naveen Kumar is the Principal Investigator of this project and supplemental issue. The author did not take part in the peer review or decision-making process for this submission and has no further conflicts to declare.

Declaration Regarding the Use of Generative AI

The authors used ChatGPT and Co-Pilot for assistance with table generation and rewording of sentences. After employing these tools, the authors carefully reviewed and edited the content as necessary and take full responsibility for the final publication.

Ethical Approval

The study was approved by the Institutional Ethics Committee (IEC) of NIMHANS. Approval numbers and dates: NIMHANS/43rd IEC (BEH.SC.DIV) 2023, dated December 8, 2023; NIMHANS/EC(BH.SC.DIV.) MEETING/2024, dated October 25, 2024; and NIMHANS/EC(BEH.SC.DIV.) MEETING/2025, dated July 1, 2025. Appropriate permissions from the concerned authorities were obtained.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This article “Short-Term Clinical Outcomes and Implementation Insights: A Hybrid Cluster Randomized Controlled Trial of an ‘Add-On Online Mental Health Training’ for Primary Care Doctors” under the research project “Multistate digitally driven capacity building program for primary mental healthcare” was funded by CSR initiative of a multinational company.

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