The Relationship between Academic Motivation and Lifelong Learning in Psychiatry Residents
Bibliographic record
Abstract
Lifelong learning (LLL) is a core training competency across the learner continuum and motivation to learn is one factor influencing the development of lifelong learners. The purpose of this study was to elucidate the relationship between LLL and academic motivation during postgraduate training, specifically in psychiatry residency training. We also studied trainee factors that may influence LLL during residency training.\nOne hundred and five (105) of 173 psychiatry residents from the University of Toronto participated in this cross-sectional study examining orientation to LLL and academic motivation, specifically intrinsic motivation (IM), extrinsic motivation (EM) and amotivation. Residents completed a questionnaire characterizing self-directed learning practices, LLL and academic motivation. \nParticipants’ orientation to LLL was significantly correlated with academic motivation total scores and with IM scores. There was no significant correlation between LLL and either EM or amotivation sub-scales. There was no significant difference in LLL or academic motivation scores based on respondents’ training year, gender, or age; however, residents participating in the research training stream had significantly higher orientations to LLL than non-research stream residents. \nTherefore, our results reinforce the association between IM and LLL during residency training. The incorporation of teaching and curricula to support autonomous motivation in postgraduate medical education may be beneficial to the development of LLL skills for practice.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".