Measuring wellbeing: A scoping review of metrics and studies measuring medical student wellbeing across multiple timepoints
Bibliographic record
Abstract
PURPOSE: Studies have demonstrated poor mental health in medical students. However, there is wide variation in study design and metric use, impairing comparability. The authors aimed to examine the metrics and methods used to measure medical student wellbeing across multiple timepoints and identify where guidance is necessary. METHODS: Five databases were searched between May and June 2021 for studies using survey-based metrics among medical students at multiple timepoints. Screening and data extraction were done independently by two reviewers. Data regarding the manuscript, methodology, and metrics were analyzed. RESULTS: 221 studies were included, with 109 observational and 112 interventional studies. There were limited studies (15.4%) focused on clinical students. Stress management interventions were the most common (40.2%). Few (3.57%) interventional studies followed participants longer than 12 months, and 38.4% had no control group. There were 140 unique metrics measuring 13 constructs. 52.1% of metrics were used only once. CONCLUSIONS: Unique guidance is needed to address gaps in study design as well as unique challenges surrounding medical student wellbeing surveys. Metric use is highly variable and future research is necessary to identify metrics specifically validated in medical student samples that reflect the diversity of today's students.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".