Conceptualizing “Preparedness for Practice”: Perspectives of Early-Career Family Physicians
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Competency based medical education (CBME) aims to produce graduates prepared for independent practice. Many equate the outcome of "preparedness for practice" with acquisition of competence. As educators evaluate the outcomes of CBME, being clear on the concept of preparedness for practice will clarify the results that are measured and assessed. This study examined how preparedness for practice is conceptualized in the literature and by family physicians (FPs) in Canada. METHODS: This multimethod qualitative descriptive study included (1) rapid review and narrative synthesis, and (2) focus groups with early-career FPs using maximum variation sampling until thematic saturation was reached. Focus groups explored the FPs' conceptualizations of preparedness for practice. Focus groups were audio-recorded, transcribed, and coded before content analysis. RESULTS: Thirty-four articles met the inclusion criteria, and 59 early-career FPs participated in the focus groups. We found no consensus on the conceptualization of preparedness for practice in the literature; however, the concept often was described as acquiring competencies for program requirements. In the literature and focus groups, we identified four themes for the conceptualization of preparedness for practice. These themes included competence, self-confidence (self-efficacy, self-concept), capability, and adaptability. CONCLUSIONS: Preparedness for practice involves an interplay of dynamic and complex constructs from competence, self-confidence, capability, and adaptability. Preparedness is more than possessing several competencies; it calls for integrating and applying competencies in complex and changing environments. This study aimed to start a discussion on what end point is desirable for residency education and proposed that the end point needs to move beyond competencies.
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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 | 0.022 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.021 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".