The Relationship Between Academic Motivation and Lifelong Learning During Residency: A Study of Psychiatry Residents
Bibliographic record
Abstract
PURPOSE: To examine the relationship between lifelong learning (LLL) and academic motivation for residents in a psychiatry residency program, trainee factors that influence LLL, and psychiatry residents' LLL practices. METHOD: Between December 2014 and February 2015, 105 of 173 (61%) eligible psychiatry residents from the Department of Psychiatry, University of Toronto, completed a questionnaire with three study instruments: an LLL needs assessment survey, the Jefferson Scale of Physician Lifelong Learning (JeffSPLL), and the Academic Motivation Scale (AMS). The AMS included a relative autonomy motivation score (AMS-RAM) measuring the overall level of intrinsic motivation (IM). RESULTS: A significant correlation was observed between JeffSPLL and AMS-RAM scores (r = 0.39, P < .001). Although there was no significant difference in JeffSPLL and AMS-RAM scores based on respondents' level of training (senior vs. junior resident), gender, or age, analysis of AMS subdomains showed that junior residents had a significantly higher score on the extrinsic motivation identification domain (mean difference [M] = 0.38; 95% confidence interval [CI] [0.01, 0.75]; P = .045; d = 0.44) compared with senior residents. Clinician scientist stream (CSS) residents had significantly higher JeffSPLL scores compared with non-CSS residents (M = 3.15; 95% CI [0.52, 5.78]; P = .020; d = 0.57). CONCLUSIONS: The use of rigorous measures to study LLL and academic motivation confirmed prior research documenting the positive association between IM and LLL. The results suggest that postgraduate curricula aimed at enhancing IM, for example, through support for learning autonomously, could be beneficial to cultivating LLL in learners.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".