College students’ mental health education refers to the educational activities aimed at cultivating college students’ good psychological quality and promoting the harmonious development of college students’ body and mind by taking reasonable educational means and using scientific educational methods on the premise of respecting the laws and characteristics of college students’ physical and psychological development. The stage of higher education is the key stage to cultivate students’ optimistic and upward psychological quality. Most college students at this stage have some psychological health problems, such as lack of security, reluctance to contact with the outside world, timidity, inferiority complex, etc. Therefore, it is very necessary to strengthen the psychological education and guidance for college students. The mental health education (MHE) quality evaluation of colleges and university in the new era is a multiple attribute decision making (MADM). The single-valued neutrosophic sets (SVNSs) is a useful tool to represent the uncertain information during the MHE) quality evaluation of colleges and university. In such paper, the single-valued neutrosophic number Aczel-Alsina power geometric (SVNNAAPG) operator and the single-valued neutrosophic number Aczel-Alsina power weighted geometric (SVNNAAPWG) operator are produced based on the Aczel-Alsina operations and classical power geometric (PG) operator under SNNs. The SVNNAAPWG operator is constructed for MADM. Eventually, numerical example of MHE quality evaluation of colleges and university in the new era and some selected comparative analysis were employed to depict the advantage of SVNNAAPWG approach. The major contribution is produced: (1) some PG operators based on the Aczel-Alsina operations and classical PG operator under SVNNs; (2) the SVNNAAPG operator and the SVNNAAPWG operator are produced based on the Aczel-Alsina operations and classical PG operator under SNNs; (3) The SVNNAAPWG operator is constructed for MADM; (4) Eventually, an example about MHE quality evaluation of colleges and university in the new era and some selected comparative studies were constructed.
WuJJ. Analysis and evaluation of the impact of integrating mental health education into the teaching of university civics courses in the context of artificial intelligence. Wirel Commun Mob Comput2022; 2022: 11.
2.
ZhangTShiZB. Research on the evaluation model of athletes youth mental health education in the context of positive psychology. Rev Psicol Deporte2022; 31: 132–41.
3.
ZhangWF. Quality evaluation of online mental health education based on reinforcement learning in the pandemic. Discrete Dyn Nat Soc2021; 2021: 1–12.
4.
WangT. Research on fuzzy comprehensive evaluation index system of mental health education for college students. J Healthcare Eng2022; 2022: 1–5.
5.
WidnallEDoddSSimmondsR, et al.A process evaluation of a peer education project to improve mental health literacy in secondary school students: study protocol. BMC Public Health2021; 21: 7.
6.
WillemsAIyamuremyeJDMisageCN, et al.Co-creation and evaluation of nationwide remote training service for mental health education of community health workers in Rwanda. Front Public Health2021; 9: 8.
7.
SharpWHargroveDSJohnsonL,et al.Mental health education: an evaluation of a classroom based strategy to modify help seeking for mental health problems. J Coll Stud Dev2006; 47: 419–38.
8.
SimpsonAReynoldsLLightI,et al.Talking with the experts: evaluation of an online discussion forum involving mental health service users in the education of mental health nursing students. Nurse Educ Today2008; 28: 633–40.
9.
SugitaKUchidaNMiyakawaA, et al., eds. Performance evaluation of www conference system for supporting remote mental health care education. In: 11th international conference on parallel and distributed systems, Fukuoka, Japan, 20–22 July 2005. Los Alamitos: IEEE Computer Soc, 2005.
10.
RhodesSBouicL. An evaluation of taster days to recruit untrained health care staff to pre-registration mental health nurse education within one UK university. Nurse Educ Today2005; 25: 556–63.
11.
RueLAEstradaSFlorenM,et al.Formative evaluation: developing measures for online family mental health recovery education. Eval Program Plann2016; 55: 27–34.
12.
SchniedermannIDehnLBMicheelS, et al.Evaluation of a supported education and employment program for adolescents and young adults with mental health problems: a study protocol of the stab project. PLoS One2022; 17: 15.
13.
O'ReillyMDograNWilliamsR, et al.Turning policy into practice: an evaluation of policy for, and delivery of, education and training for the staff of specialist child and adolescent mental health services in England. Evid Policy2010; 6: 505–25.
14.
ReevesSFreethD. Re-examining the evaluation of interprofessional education for community mental health teams with a different lens: understanding presage, process and product factors. J Psychiatr Ment Health Nurs2006; 13: 765–70.
15.
ReutzelTJDesaiAWorkmanG, et al.Medication management in primary and secondary schools: evaluation of mental health related in-service education in local schools. J Sch Nurs2008; 24: 239–48.
16.
LakemanR. The weaponisation of student evaluation of teaching: implications for the education of mental health professionals. I J Ment Health Nurs2022; 31: 34–35.
17.
O'BrienNLawlorMChambersF,et al.State of mind Ireland-higher education: a mixed-methods longitudinal evaluation of a positive mental health intervention. Int J Environ Res Public Health2020; 17: 23.
18.
VermaRSharmaBD. Fuzzy generalized prioritized weighted average operator and its application to multiple attribute decision making. Int J Intell Syst2014; 29: 26–49.
19.
GargH. Linguistic interval-valued Pythagorean fuzzy sets and their application to multiple attribute group decision-making process. Cogn Comput2020; 12: 1313–37.
20.
GulistanMKhanS. Extentions of neutrosophic cubic sets via complex fuzzy sets with application. Complex Intell Syst2020; 6: 309–20.
21.
NezhadMZNazarian-JashnabadiJRezazadehJ, et al.Assessing dimensions influencing IoT implementation readiness in industries: a fuzzy DEMATEL and fuzzy AHP analysis. J Soft Comput Decis Anal2023; 1: 102–23.
22.
KhanQJabeenK. Schweizer-Sklar aggregation operators with unknown weight for picture fuzzy information. J Innov Res Math Comput Sci2022; 1: 83–106.
23.
SarfrazMUllahKAkramM, et al.Prioritized aggregation operators for intuitionistic fuzzy information based on Aczel-Alsina t-norm and t-conorm and their applications in group decision-making. Symmetry-Basel2022; 14: 21.
24.
HussainAUllahKMubasherM, et al.Interval-valued Pythagorean fuzzy information aggregation based on Aczel-Alsina operations and their application in multiple attribute decision making. IEEE Access2023; 11: 34575–94.
25.
HussainAWangHLUllahK, et al.Maclaurin symmetric mean aggregation operators based on novel Frank t-norm and t-conorm for intuitionistic fuzzy multiple attribute group decision-making. Alex Eng J2023; 71: 535–50.
26.
MahmoodTAliZUllahK, et al.Complex Pythagorean fuzzy aggregation operators based on confidence levels and their applications. Math Biosci Eng2022; 19: 1078–107.
27.
UllahKAliZMahmoodT, et al.Methods for multi-attribute decision making, pattern recognition and clustering based on T-spherical fuzzy information measures. J Intell Fuzzy Syst2022; 42: 2957–77.
28.
AliZMahmoodTKhanMB. Three-way decisions with complex q-rung orthopair 2-tuple linguistic decision-theoretic rough sets based on generalized Maclaurin symmetric mean operators. AIMS Math2023; 8: 17943–80.
29.
GargHAliZMahmoodT, et al.Schweizer-sklar prioritized aggregation operators for intuitionistic fuzzy information and their application in multi-attribute decision-making. Alex Eng J2023; 67: 229–40.
30.
MahmoodTAliZ. Multi-attribute decision-making methods based on Aczel-Alsina power aggregation operators for managing complex intuitionistic fuzzy sets. Comput Appl Math2023; 42: 34.
31.
MahmoodTRehmanUUAliZ. A novel complex fuzzy n-soft sets and their decision-making algorithm. Complex Intell Syst2021; 7: 2255–80.
32.
MahmoodTur RehmanUAliZ. Analysis and application of Aczel-Alsina aggregation operators based on bipolar complex fuzzy information in multiple attribute decision making. Inf Sci2023; 619: 817–33.
33.
AczélJAlsinaC. Characterizations of some classes of quasilinear functions with applications to triangular norms and to synthesizing judgements. Aequ Math1982; 25: 313–15.
34.
SenapatiTChenGYYagerRR. Aczel-alsina aggregation operators and their application to intuitionistic fuzzy multiple attribute decision making. Int J Intell Syst2022; 37: 1529–51.
35.
SenapatiTChenGYMesiarR,et al.Novel Aczel-Alsina operations-based interval-valued intuitionistic fuzzy aggregation operators and their applications in multiple attribute decision-making process. Int J Intell Syst2022; 37: 5059–81.
36.
WangYLBonyahEKhayyatM, et al.A novel approach on decision support system based on the Aczel-Alsina aggregation operators and their applications to supplier selection problems. J Funct Spaces2022; 2022: 1–26.
37.
ShiXMAliZMahmoodT,et al.Power aggregation operators of interval-valued Atanassov-intuitionistic fuzzy sets based on Aczel-Alsina t-norm and t-conorm and their applications in decision making. Int J Comput Intell Syst2023; 16: 20.
38.
SenapatiTChenGYMesiarR, et al.Novel Aczel-Alsina operations-based hesitant fuzzy aggregation operators and their applications in cyclone disaster assessment. Int J Gen Syst2022; 51: 511–46.
39.
KhanAAshrafSAbdullahS, et al.A novel decision aid approach based on spherical hesitant fuzzy Aczel-Alsina geometric aggregation information. AIMS Math2023; 8: 5148–74.
40.
NaeemMKhanYAshrafS, et al.A novel picture fuzzy Aczel-Alsina geometric aggregation information: application to determining the factors affecting mango crops. AIMS Math2022; 7: 12264–88.
41.
SenapatiT. Approaches to multi-attribute decision-making based on picture fuzzy Aczel-Alsina average aggregation operators. Comput Appl Math2022; 41: 19.
42.
JabeenKUllahKAkramM,et al.Interval valued picture fuzzy Aczel-Alsina aggregation operators and their application by using the multiattribute decision making problem. J Math2023; 2023: 1–23.
43.
HussainAUllahKAlshahraniMN, et al.Novel Aczel-Alsina operators for Pythagorean fuzzy sets with application in multi-attribute decision making. Symmetry-Basel2022; 14: 20.
44.
SenapatiTMishraARSahaA, et al.Construction of interval-valued Pythagorean fuzzy Aczel-Alsina aggregation operators for decision making: a case study in emerging it software company selection. Sadhana-Acad Proc Eng Sci2022; 47: 18.
45.
JinHHHussainAUllahK,et al.Novel complex Pythagorean fuzzy sets under Aczel-Alsina operators and their application in multi-attribute decision making. Symmetry-Basel2023; 15: 37.
46.
YongRYeJDuSG, et al.Aczel-Alsina weighted aggregation operators of simplified neutrosophic numbers and its application in multiple attribute decision making. CMES - Comput Model Eng Sci2022; 132: 569–84.
47.
AshrafSAhmadSNaeemM, et al.Novel EDAS methodology based on single-valued neutrosophic Aczel-Alsina aggregation information and their application in complex decision-making. Complexity2022; 2022: 1–18.
48.
SmarandacheFA. Unifying field in logics. Neutrosophy: Neutrosophic probability, set and logic. Rehoboth: American Research Press, 1999.
PengJJWangJQWangJ, et al.Simplified neutrosophic sets and their applications in multi-criteria group decision-making problems. Int J Syst Sci2016; 47: 2342–58.
53.
HigginsACallaghanPdeVriesJ, et al.Evaluation of mental health recovery and wellness recovery action planning education in Ireland: a mixed methods pre-postevaluation. J Adv Nurs2012; 68: 2418–28.
54.
HigginsACarrollMSharekD. Impact of perinatal mental health education on student midwives’ knowledge, skills and attitudes: a pre/post evaluation of a module of study. Nurs Educ Today2016; 36: 364–69.
55.
JohnsonCLHartLMRossettoA, et al.Lessons learnt from the field: a qualitative evaluation of adolescent experiences of a universal mental health education program. Health Educ Res2021; 36: 126–39.
56.
FeltonAWrightN. Simulation in mental health nurse education: the development, implementation and evaluation of an educational innovation. Nurse Educ Pract2017; 26: 46–52.
57.
FogartyASteelZWardPB, et al.Trauma and mental health awareness in emergency service workers: a qualitative evaluation of the behind the seen education workshops. Int J Environ Res Public Health2021; 18: 16.
58.
HardySAKingsnorthR. Mental health nurses can increase capability and capacity in primary care by educating practice nurses: an evaluation of an education programme in England. J Psychiatr Ment Health Nurs2015; 22: 270–77.
59.
BanninkRJoosten-van ZwanenburgEvan de Looij-JansenP, et al.Evaluation of computer-tailored health education (‘e-health4uth’) combined with personal counselling (‘e-health4uth + counselling’) on adolescents’ behaviours and mental health status: design of a three-armed cluster randomised controlled trial. BMC Public Health2012; 12: 8.
60.
BrownLBlytheCGreggL,et al.Inter-professional practice education: evaluation of a music therapy student's contribution to occupational therapy practice on a mental health rehabilitation unit. Br J Occup Ther2017; 80: 124–124.
61.
ChambersMConnorSLDavrenM. An evaluation of a combined education and multi-project practice development programme in mental health. J Psychiatr Ment Health Nurs2006; 13: 364–71.
62.
D'SouzaRHustigHMontgomeryW. Telehealth in rural and remote mental health: a review of the use of telepsychiatry in education, training, treatment, discharge planning and a health economic evaluation of the telemedicine service. Aust N Z J Psychiatry2001; 35: A7–A7.
63.
EvansRBrockmanRGreyJ, et al.A cluster randomised controlled trial of the wellbeing in secondary education (wise) project - an intervention to improve the mental health support and training available to secondary school teachers: protocol for an integrated process evaluation. Trials2018; 19: 13.
64.
BolturkEKarasanA. Prioritization of investment alternatives for a hospital by using neutrosophic codas method. J Mult-Valued Log Soft Comput2019; 33: 381–96.
65.
StanujkicDKarabasevicDPopovicG, et al.A single-valued neutrosophic extension of the edas method. Axioms2021; 10: 13.
66.
Ul HaqRSSaeedMMateenN, et al.Sustainable material selection with crisp and ambiguous data using single-valued neutrosophic-merec-marcos framework. Appl Soft Comput2022; 128: 109546.
67.
HezamIMMishraARRaniP, et al.An integrated decision support framework using single-valued neutrosophic-MASWIP-COPRAS for sustainability assessment of bioenergy production technologies. Expert Syst Appl2023; 211: 118674.
68.
SalabunWUrbaniakK, eds. A new coefficient of rankings similarity in decision-making problems. In: 20th annual international conference on computational science (ICCS), Amsterdam, Netherlands, 03–05 June 2020. Cham: Springer International Publishing Ag, 2020.
69.
SałabunWWątróbskiJShekhovtsovA. Are MCDA methods benchmarkable? A comparative study of TOPSIS, VIKOR, COPRAS, and PROMETHEE II methods. Symmetry2020; 12: 1549.
70.
TverskyAKahnemanD. Prospect theory: an analysis of decision under risk. Econometrica1979; 47: 263–91.
71.
KahnemanT. Advances in prospect theory: cumulative representation of uncertainty. J Risk Uncertain1992; 5: 297–323.
72.
BleichrodtHCilloADiecidueE. A quantitative measurement of regret theory. Manage Sci2010; 56: 161–75.
73.
WangHLMahmoodTUllahK. Improved CoCoSo method based on Frank softmax aggregation operators for T-spherical fuzzy multiple attribute group decision-making. Int J Fuzzy Syst2023; 25: 1275–310.
74.
SarkarAMoslemSEsztergar-KissD, et al.A hybrid approach based on dual hesitant q-rung orthopair fuzzy Frank power partitioned Heronian mean aggregation operators for estimating sustainable urban transport solutions. Eng Appl Artif Intell2023; 124: 23.
75.
AkhtarMRahimMAlanziAM, et al.Dombi aggregation operators for p, q r-spherical fuzzy sets: application in the stability assessment of cryptocurrencies. IEEE Access2024; 12: 10366–82.
76.
ZangYQZhaoJMJiangWC,et al.Advanced linguistic complex T-spherical fuzzy Dombi-weighted power-partitioned Heronian mean operator and its application for emergency information quality assessment. Sustainability2024; 16: 35.
77.
WangHLXuTJFengLQ, et al.Aczel-Alsina Hamy mean aggregation operators in T-spherical fuzzy multi-criteria decision-making. Axioms2023; 12: 23.
78.
KhanMIAlmazrooeiAELiYH, et al.Hamy mean operator under complex picture fuzzy environment and its application to disaster management program. J Intell Fuzzy Syst2023; 45: 10411–36.
79.
MoslemS. A novel parsimonious spherical fuzzy analytic hierarchy process for sustainable urban transport solutions. Eng Appl Artif Intell2024; 128: 107447.
