Supporting early academic family medicine careers with the clinician scholar enhanced-skills program
Bibliographic record
Abstract
CONTEXT: The Clinician Scholar Program (CSP) is an enhanced-skills (R3) residency program to train clinician researchers/educators/leaders for academic family practice. This article intends to share Laval University's CSP development and evaluation strategy, and provide recommendations for similar innovations in other disciplines/settings. METHODS: This article uses Kern's model to present the program development, and a program-oriented approach for program evaluation, carried from 2011 to 2017 using descriptive data. Questionnaires, reflexive texts and an Objective Structured Teaching Exam supported data collection. RESULTS: Seven CSP graduates and 14 controls participated in the program evaluation. Residents were highly satisfied with the program, nevertheless they suggested to allow physicians to come back for training later in career. The CSP enriched knowledge, skills and attitudes about academic practice. CSP increased residents' entrustment level about academic competencies. All graduates joined an academic practice within five years of program completion. CONCLUSION: Key recommendations to implement similar programs include academic medicine core training, project- based learning with learner-centered objectives, relevant and authentic learning and assessment, and multi-level program evaluation approach. Programs should consider concomitant graduate studies and opportunity to offer such training after a few years of clinical practice to meet other needs at a timely stage of career.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".