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.
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 | 0.041 | 0.148 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.038 | 0.033 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".