Medical student well-being and lifelong learning: A motivational perspective
Bibliographic record
Abstract
Background: Medical school poses many pressures and challenges for individuals aspiring to health careers. Only some students, however, experience high stress and exhaustion, whereas others adaptively respond to schooling demands and engage in lifelong learning practices. By drawing on three motivation theories - self-determination theory, self-theories of ability, and achievement goal theory - this study examined the relations among motivational constructs, stress, exhaustion, and lifelong learning in medical students. Methods: All medical students in a 4-year program were invited to complete a questionnaire containing measures of psychological need satisfaction, self-theories of ability, achievement goals, stress, exhaustion, lifelong learning, and background characteristics. Using structural equation modeling, we tested a structural model that combined the three motivation theories to explain stress, exhaustion, and lifelong learning in medical students. Results: A total of 267 medical students participated in the study (response rate 42%). The results largely confirmed the hypothesized relations, revealing that unmet psychological needs and a fixed mind-set were associated with maladaptive cognitions (i.e., the pursuit of avoidance goals) and psychological distress (i.e., high stress and exhaustion). In contrast, psychological need satisfaction and a growth mind-set had distinct pathways to beneficial cognitions (i.e., mastery approach goals) and lifelong learning practices in medical students. Discussion: Adaptive motivations, cultivated through personal and environmental factors, may help to protect medical students from psychological distress and enhance their growth as lifelong learners. Understanding the mechanisms and pathways to desirable and undesirable outcomes in medical students is critical for creating learning environments that will serve these students well.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".