Measuring medical student wellbeing longitudinally: a psychometric systematic review of commonly used scales
Bibliographic record
Abstract
Background: Longitudinal measurements of medical student wellbeing are needed to evaluate the impacts of training and potential interventions, but the psychometric evidence underlying commonly used wellbeing scales is unclear, impairing selection decisions. We therefore synthesized the psychometric evidence of the most common scales employed to measure self-reported medical student wellbeing longitudinally. Methods: We conducted a psychometric systematic review based on the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) guidelines. We searched seven databases and gray literature in March 2023 for psychometric studies in medical students of 53 scales. Two independent reviewers completed screening and data extraction and resolved conflicts via discussion. We assessed study quality and psychometrics using COSMIN methodology and pooled results for internal consistency and test-retest reliability when there were ≥2 studies per scale. Results: Of 2374 abstracts, we included 133 studies. Over a quarter (26.4%) of study scales lacked psychometric evidence in medical students. Internal consistency was the most studied property (118 studies), while there were no studies on measurement error. There was sufficient evidence of internal consistency for 30 scales and construct validity for 34 scales. However, there were only 1-6 scales with sufficient evidence for each of the remaining properties. Study quality varied widely and only 20 of them reported participant ethno-racial identity. Conclusions: Many scales commonly used to measure medical student wellbeing longitudinally lack medical student-specific psychometric evidence. Among those that do, few have any evidence beyond internal consistency and construct validity. Future psychometric studies are needed in diverse populations to better inform scale selection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.072 | 0.293 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.013 |
| Bibliometrics | 0.023 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".