Incorporating Lifelong Learning From Residency to Practice: A Qualitative Study Exploring Psychiatry Learners' Needs and Motivations
Bibliographic record
Abstract
INTRODUCTION: There has been an increased focus on lifelong learning (LLL) as a core competency to develop master learners in medical education across the learner continuum. The purpose of this study was to explore the perceptions of psychiatry residents and faculty about LLL implementation, motivation, and training needs. METHODS: This qualitative study was conducted in a large, urban, multisite psychiatry training program as part of a larger mixed methods study of LLL in psychiatry education. Using a purposive sampling approach, psychiatry residents were recruited to participate in focus groups; early career psychiatrists and psychiatry educators were recruited to participate in semistructured interviews. Content analysis of interviews and focus groups was done using the iterative, inductive method of constant comparative analysis. RESULTS: Of the 34 individuals participating in the study, 23 were residents, six were psychiatry educators, and five were early career psychiatrists. Three predominant themes were identified in participants' transcripts related to (1) the need for LLL training in residency training; (2) the implementation of LLL in residency training and practice; and (3) the spectrum of motivation for LLL from residency training into practice. DISCUSSION: This study identified the lack of preparation for LLL in residency training and the impact of this gap for psychiatrists transitioning into practice. All participants described the importance of integrating LLL training within clinical rotations and the importance of grounding LLL within the clinical workplace early in residency training to support the delivery of effective, high-quality patient care.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".