Medical student wellness assessment beyond anxiety and depression: A scoping review
Bibliographic record
Abstract
BACKGROUND: A significant increase in distress and mental health illnesses has been identified in medical students during their training. As a result, medical schools have attempted to understand factors linked to well-being. Wellness questionnaires present a useful approach to identifying students with risk factors for mental health to provide appropriate resources for support and referrals. This study aims to identify validated questionnaires in the literature that measure medical student wellness. METHODS: A scoping review methodology was selected and an exhaustive search of MEDLINE, Embase, CINAHL, APA PsycInfo, EPIC, and Education Source, was performed from 1999 to May 27, 2021. A compilation of validated wellness evaluation tools, surveys and questionnaires assessing wellness beyond depression and anxiety was reviewed. All validated methods of wellness assessment for medical students were included. RESULTS: 5,001 studies were identified once duplicate records were removed. After applying inclusion and exclusion criteria, 23 articles were included in a qualitative synthesis and explored in detail. The following six validated questionnaires measuring the wellness of medical school students are reported and discussed: the Medical Student Stress Profile (MSSP), the Medical Student Stress Questionnaire (MSSQ), the Medical Student Well-Being Index (MSWBI), the Perceived Medical School Stress (PMSS), the Perceived Stress Scale for Medical Students (PSSMS), and the Oldenburg Burnout Inventory-Medical Student Version (OLBI-MS). These validated questionnaires provide various aspects to the assessment of wellbeing. CONCLUSIONS: Wellbeing evaluations are reliable in identifying medical students who are at risk for mental health illnesses but must be chosen carefully based on contexts, academic environment and student population. A direct comparison between validated questionnaires for student wellbeing is not possible and individual medical schools must determine the appropriateness and validity of such tools based on population-specific characteristics and demands.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.021 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".