Contributions of psychological needs, self-compassion, leisure-time exercise, and achievement goals to academic engagement and exhaustion in Canadian medical students
Bibliographic record
Abstract
PURPOSE: To investigate the contributions of psychological needs (autonomy, competence, and relatedness) and coping strategies (self-compassion, leisure-time exercise, and achievement goals) to engagement and exhaustion in Canadian medical students. METHODS: This was an observational study. Two hundred undergraduate medical students participated in the study: 60.4% were female, 95.4% were 20-29 years old, and 23.0% were in year 1, 30.0% in year 2, 21.0% in year 3, and 26.0% in year 4. Students completed an online survey with measures of engagement and exhaustion from the Oldenburg Burnout Inventory-student version; autonomy, competence, and relatedness from the Basic Psychological Needs Scale; self-compassion from the Self-Compassion Scale-short form; leisure-time exercise from the Godin Leisure-Time Exercise Questionnaire; and mastery approach, mastery avoidance, performance approach, and performance avoidance goals from the Achievement Goals Instrument. Descriptive and inferential analyses were performed. RESULTS: The need for competence was the strongest predictor of student engagement (β= 0.35, P= 0.000) and exhaustion (β= -0.33, P= 0.000). Students who endorsed mastery approach goals (β= 0.21, P= 0.005) and who were more self-compassionate (β= 0.13, P= 0.050) reported greater engagement with their medical studies. Students who were less self-compassionate (β= -0.32, P= 0.000), who exercised less (β= -0.12, P= 0.044), and who endorsed mastery avoidance goals (β= 0.22, P= 0.003) reported greater exhaustion from their studies. Students' gender (β= 0.18, P= 0.005) and year in medical school (β= -0.18, P= 0.004) were related to engagement, but not to exhaustion. CONCLUSION: Supporting students' need for competence and raising students' awareness of self-compassion, leisure-time exercise, and mastery approach goals may help protect students from burnout-related exhaustion and enhance their engagement with their medical school studies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".