Motivations to conduct research and burnout in medical education: a mixed methods study of students and residents
Bibliographic record
Abstract
Background: Burnout is on the rise in medical training as workload increases. One such demand is the pressure for research productivity earlier in training. However, little is known about the impacts of this trend and its mediating factors as trainees progress. Important influences may be motivation sources and supports, since intrinsic motivation is linked to well-being. This mixed methods study investigated associations between burnout and motivations for conducting research in a sample of medical students and residents in one academic centre. Methods: Participants completed an online survey including validated scales for measuring burnout (Maslach Burnout Inventory) and intrinsic research motivation (using the Situational Motivation Scale) along with open response items to identify supports for autonomy, competence, and relatedness in the process of conducting research. Results were synthesized from the statistical and thematic analyses, using the framework of self-determination theory. Results: Forty-three survey responses were analyzed. Overall prevalence of burnout was high (60.5%) and evidenced a progressive impact, with a significant increase in depersonalization among residents compared to medical students. Participants articulating more intrinsic reasons for doing research had lower levels of burnout. Intrinsically motivated individuals were more likely to have increased relational and academic supports and less likely to internalize barriers to conducting research. Residents expressed more competence in their ability to do research but less relational supports. Discussion/Conclusions: Burnout is a multifaceted condition requiring multiple mitigation strategies. This study identified a correlation between research motivation and burnout and mediating protective factors. These findings can inform study of interventions focussed on targeted motivational supports to advance research training in medical education.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".