Association between Overall Academic Achievement, Cognitive, and Non-Cognitive Variables among Medical and Allied Health Students: A 10-Year Review
DOI:
https://doi.org/10.31674/mjn.2023.v15i02.018Abstract
Background: Medical and allied health institutions employ cognitive measures to select student applicants who can fulfill curriculum components and pass the licensure examination. Secondary school certificate (SSC) grades and preadmission aptitude test (PAT) scores have been in use without systematic evidence. Nevertheless, the connection between non-cognitive elements and academic achievement is not clear. Objectives: This study explored the association between cognitive and non-cognitive aspects of academic achievement in medical and allied health programs. Methods: A narrative review design was applied, and the PubMed/MEDLINE database was searched. The eligibility criteria were undergraduate courses in medical and allied health programs, examining at least one cognitive (SSC scores and preadmission) and non-cognitive (age, gender, parity, parental occupation, lottery, non-cognitive personality test, and letter of recommendation) factor, measuring academic achievement and associational outcomes. Studies were screened by title, abstract, and full text, and data were extracted using a novel data extraction form. Results: About 22 studies were included, involving a sample of 29,152 students were reviewed. There were mixed results on the examined factors, with SSC grades, PAT scores, being female, and being of younger age appearing to have diminishing consistency in the mentioned order across the reviewed studies. There was a paucity of studies on the examined non-cognitive elements. Conclusion: There is insufficient evidence to recommend the exclusive use of any single cognitive or non-cognitive element for admissions decisions. A combination of SSC grades, PAT scores, female gender, and younger age should be considered and weighed in the mentioned order of importance.
Keywords:
Academic Success, Aptitude, Certification, Schools, StudentsDownloads
References
Adam, J., Bore, M., Childs, R., Dunn, J., Mckendree, J., Munro, D., & Powis, D. (2015). Predictors of professional behaviour and academic outcomes in a UK medical school: A longitudinal cohort study. Medical Teacher, 37(9), 868-880. https://doi.org/10.3109/0142159X.2015.1009023
Al-Alwan, I., Al-Kishi, M., Tamim, H., Magzoub, M., & Elzubeir, M. (2013). Health sciences and medical college preadmission criteria and prediction of in-course academic performance: a longitudinal cohort study. Advances in Health Sciences Education, 18(3), 427-438. https://doi.org/10.1007/s10459-012-9380-1
Alhurishi, S. A., Aljuraiban, G. S., Alshaikh, F. A., Almutairi, M. M., & Almutairi, K. M. (2021). Predictors of students’ academic achievements in allied health professions at King Saud University: a retrospective cohort study. BMC Medical Education, 21(1), 1-7. https://doi.org/10.1186/s12909-021-02525-x
Almarabheh, A., Shehata, M. H., Ismaeel, A., Atwa, H., & Jaradat, A. (2022). Predictive validity of admission criteria in predicting academic performance of medical students: a retrospective cohort study. Frontiers in Medicine, 9, 971926. https://doi.org/10.3389/fmed.2022.971926
Al-Qahtani, M., & Alanzi, T. (2018). Comparisons of the predictive values of admission criteria for academic achievement among undergraduate students of health and non-health science professions: a longitudinal cohort study. Psychology Research and Behavior Management, 12(1), 1-6. https://doi.org/10.2147/PRBM.S183651
Alshammari, F., Saguban, R., Pasay-an, E., Altheban, A., & Al-Shammari, L. (2017). Factors affecting the academic performance of student nurses: A cross-sectional study. Journal of Nursing Education and Practice, 8(1), 60. https://doi.org/10.5430/jnep.v8n1p60
Ang, W., Chew, H., Rusli, K., Ng, W., Zheng, Z., Liaw, S., Ang, N., & Lau, Y. (2022). Spotlight on noncognitive skills: Views from nursing students and educators. Nurse Education Today, 117(1), e105486. https://doi.org/10.1016/j.nedt.2022.105486
Barber, C., Burgess, R., Mountjoy, M., Whyte, R., Vanstone, M., & Grierson, L. (2022). Associations between admissions factors and the need for remediation. Advances in Health Sciences Education, 27(2), 475-489. https://doi.org/10.1007/s10459-022-10097-8
Bekele, A. T., Beza, S. W., Gedamu, S., & Berndt, M. (2023). Predictors of College Academic Achievement for Medical Students: The Case of Gondar University, College of Medicine and Health Sciences, Ethiopia. Advances in Medical Education and Practice, 603-613. https://doi.org/10.2147/AMEP.S406031
Callwood, A., Jeevaratnam, K., Kotronoulas, G., Schneider, A., Lewis, L., & Nadarajah, V. (2018). Personal domains assessed in multiple mini interviews (MMIs) for healthcare student selection: a narrative synthesis systematic review. Nurse Education Today, 64(1), 56–64. https://doi.org/10.1016/j.nedt.2018.01.016
De-Visser, M., Fluit, C., Cohen-Schotanus, J., & Laan, R. (2018). The effects of a non-cognitive versus cognitive admission procedure within cohorts in one medical school. Advances in Health Sciences Education, 23(1), 187-200. https://doi.org/10.1007/s10459-017-9782-1
Edwards, D., Friedman, T., & Pearce, J. (2013). Same admissions tools, different outcomes: a critical perspective on predictive validity in three undergraduate medical schools. BMC Medical Education, 13(1), e173. https://doi.org/10.1186/1472-6920-13-173
Finn, G., Mwandigha, L., Paton, L., & Tiffin, P. (2018). The ability of 'non-cognitive' traits to predict undergraduate performance in medical schools: a national linkage study. BMC Medical Education, 18(1), e93. https://doi.org/10.1186/s12909-018-1201-7
Gale, J., Ooms, A., Grant, R., Paget, K., & Marks-Maran, D. (2016). Student nurse selection and predictability of academic success: the multiple mini interview project. Nurse Education Today, 40(1), 123–127. https://doi.org/10.1016/j.nedt.2016.01.031
Hollinger-Smith, L. M., Patterson, B. J., Morin, K. H., & Scott, C. J. (2023). Cognitive and Noncognitive Factors Influencing Nursing Students’ Academic Success: Structural Equation Model Analysis. Nursing Education Perspectives. https://doi.org/10.1097/01.NEP.0000000000001121
Hsieh, P., Wang, Y., & Huang, T. (2023). Exploring key factors influencing nursing students' cognitive load and willingness to serve older adults: Cross-sectional descriptive correlational study. JMIR Serious Games, 11(1), e43203. https://doi.org/10.2196/43203
Kim, T., Chang, J., Myung, S., Chang, Y., Park, K., Park, W., & Shin, C. (2016). Predictors of undergraduate and postgraduate clinical performance: A longitudinal cohort study. Journal of Surgical Education, 73(4), 715-720. https://doi.org/10.1016/j.jsurg.2016.03.006
Kreiter, C., O'Shea, M., Bruen, C., Murphy, P., & Pawlikowska, T. (2018). A meta-analytic perspective on the valid use of subjective human judgement to make medical school admission decisions. Medical Education Online, 23(1), e1522225. https://doi.org/10.1080/10872981.2018
Krings, R., Huwendiek, S., Walsh, N., Stricker, D., & Berendonk, C. (2020). Predictive power of high school educational attainment and the medical aptitude test for performance during the Bachelor program in human medicine at the University of Bern: a cohort study. Swiss Medical Weekly, 150(1), e20389. https://doi.org/10.4414/smw.2020.20389
Lancia, L., Caponnetto, V., Dante, A., Mattei, A., La-cerra, C., Cifone, M., & Petrucci, C. (2018). Analysis of factors potentially associated with nursing students' academic outcomes: a thirteen-year retrospective multi-cohort study. Nurse Education Today, 70(1), 115-120. https://doi.org/10.1016/j.nedt.2018.08.020
Liu, X., Codd, C., & Mills, C. (2018). Incremental effect of academic predictors on nursing admission assessment. Nurse Educator, 43(6), 292-296. https://doi.org/10.1097/NNE.0000000000000502
Ma, L., Wang, Y., Yang, Z., Huang, D., Weng, H., & Zeng, A. (2020). Methodological quality (risk of bias) assessment tools for primary and secondary medical studies: what are they and which is better? Military Medical Research, 7(1), e7. https://doi.org/10.1186/s40779-020-00238-8
Mercer, A., Abbott, P., & Puddey, I. (2013). Relationship of selection criteria to subsequent academic performance in an Australian undergraduate dental school. European Journal of Dental Education, 17(1), 39-45. https://doi.org/10.1111/eje.12005
Mwandigha, L., Tiffin, P., Paton, L., Kasim, A., & Bohnke, J. (2018). What is the effect of secondary (high) schooling on subsequent medical school performance? a national, UK-based, cohort study. BMJ Open, 8(5), e020291. https://doi.org/10.1136/bmjopen-2017-020291
Patterson, F., Griffin, B., & Hanson, M. (2018). Opening editorial: selection and recruitment in medical education. MedEdPublish, 7(1), 1. https://doi.org/10.15694/mep.2018.0000222.1
Plouffe, R., Hammond, R., Goldberg, H., & Chahine, S. (2018). What matters from admissions? identifying success and risk among canadian dental students. Journal of Dental Education, 82(5), 515-523. https://doi.org/10.21815/JDE.018.057
Price, M., & Park, S. (2018). Can noncognitive components of admissions data predict dental student performance and postdoctoral program placement? Journal of Dental Education, 82(10), 1051-1058. https://doi.org/10.21815/JDE.018.112
Roach, A., Rose, A., Beiers-jones, W. S., Licaycay, W., & Nielsen, A. (2019). Incorporating group interviews into holistic review in baccalaureate nursing school admissions. Nursing Education Perspectives, 40(2), 125-127. https://doi.org/10.1097/01.NEP.0000000000000338
Sladek, R., Bond, M., Frost, L., & Prior, K. (2016). Predicting success in medical school: a longitudinal study of common Australian student selection tools. BMC Medical Education, 16(1), e187. https://doi.org/10.1186/s12909-016-0692-3
Stankus, J., Hamner, M., Stankey, M., & Mancuso, P. (2019). Successful modeling of factors related to recruitment and retention of prenursing students. Nurse Educator, 44(3), 147-150. https://doi.org/10.1097/NNE.0000000000000579
Tetzlaff, J., Page, M., & Moher, D. (2020). The PRISMA 2020 statement: development of and key changes in an updated guideline for reporting systematic reviews and meta-analyses. Value Health, 23(1), S312–S313. https://doi.org/10.1016/j.jval.2020.04.1154
Vian, C. V., binti Zulkifli, A. J., bin Hishamudin, A. N., Xian, L. F., Barry, J. A. A. H., Yee, O. T., ... & Mohandas, K. (2019). To compare the cognitive ability of preclinical medical students at different times of the working day and correlate this with their morningness-eveningness status. Malaysian Journal of Medical Research (MJMR), 3(1), 29-40. https://doi.org/10.31674/mjmr.2019.v03i01.005
Vos, C., Wouters, A., Jonker, M., de-Haan, M., Westerhof, M., Croiset, G., & Kusurkar, R. (2019). Bachelor completion and dropout rates of selected, rejected and lottery-admitted medical students in the Netherlands. BMC Medical Education , 19(1), e80. https://doi.org/10.1186/s12909-01
Wambuguh, O., Eckfield, M., & Van-hofwegen, L. (2016). Examining the importance of admissions criteria in predicting nursing program success. International Journal of Nursing Education Scholarship, 13(1), 87–96. https://doi.org/10.1515/ijnes-2015-0088
Yousafzai, I., & Jamil, B. (2019). Relationship between admission criteria and academic performance: a correlational study in nursing students. Pakistan Journal of Medical Sciences, 35(3), 858-861. https://doi.org/10.12669/pjms.35.3.217
Zamanzadeh, V., Ghahramanian, A., Valizadeh, L., Bagheriyeh, F., & Lynagh, M. (2020). A scoping review of admission criteria and selection methods in nursing education. BMC Nursing, 19(1), 121. https://doi.org/10.1186/s12912-020-00510-1
Žuljević, M., & Buljan, I. (2022). Academic and non-academic predictors of academic performance in medical school: an exploratory cohort study. BMC Medical Education, 22(1), e366. https://doi.org/10.1186/s12909-022-03436-1
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 The Malaysian Journal of Nursing (MJN)
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
