The do’s, don’t and don’t knows of supporting transition to more independent practice
Bibliographic record
Abstract
INTRODUCTION: Transitions are traditionally viewed as challenging for clinicians. Throughout medical career pathways, clinicians need to successfully navigate successive transitions as they become progressively more independent practitioners. In these guidelines, we aim to synthesize the evidence from the literature to provide guidance for supporting clinicians in their development of independence, and highlight areas for further research. METHODS: Drawing upon D3 method guidance, four key themes universal to medical career transitions and progressive independence were identified by all authors through discussion and consensus from our own experience and expertise: workplace learning, independence and responsibility, mentoring and coaching, and patient perspectives. A scoping review of the literature was conducted using Medline database searches in addition to the authors' personal archives and reference snowballing searches. RESULTS: 387 articles were identified and screened. 210 were excluded as not relevant to medical transitions (50 at title screen; 160 at abstract screen). 177 full-text articles were assessed for eligibility; a further 107 were rejected (97 did not include career transitions in their study design; 10 were review articles; the primary references of these were screened for inclusion). 70 articles were included of which 60 provided extractable data for the final qualitative synthesis. Across the four key themes, seven do's, two don'ts and seven don't knows were identified, and the strength of evidence was graded for each of these recommendations. CONCLUSION: The two strongest messages arising from current literature are first, transitions should not be viewed as one moment in time: career trajectories are a continuum with valuable opportunities for personal and professional development throughout. Second, learning needs to be embedded in practice and learners provided with authentic and meaningful learning opportunities. In this paper, we propose evidence-based guidelines aimed at facilitating such transitions through the fostering of progressive independence.
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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 | 0.089 | 0.266 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.020 | 0.026 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".