General practice residents’ perspectives on their professional identity formation: a qualitative study
Bibliographic record
Abstract
Objectives To move beyond professionalism as a measurable competency, medical educators have highlighted the importance of forming a professional identity, in which learners come to ‘think, act, and feel like physicians’. This socialisation process is known as professional identity formation (PIF). Few empirical studies on PIF in residency have been undertaken. None of these studies focused on PIF during the full length of GP training as well as the interplay of concurrent socialising factors. Understanding the socialisation process involved in the development of a resident’s professional identity and the roles of influencing factors and their change over time could add to a more purposeful approach to PIF. Therefore, we aimed to investigate the process of PIF during the full length of General Practice (GP) training and which factors residents perceive as influential. Design A qualitative descriptive study employing focus group interviews. Setting Four GP training institutes across the Netherlands. Participants Ninety-two GP residents in their final training year participated in 12 focus group interviews. Results Study findings indicated that identity formation occurs primarily in the workplace, as residents move from doing to becoming and negotiate perceived norms. A tapestry of interrelated influencing factors—most prominently clinical experiences, clinical supervisors and self-assessments—changed over time and were felt to exert their influence predominantly in the workplace. Conclusions This study provides deeper empirical insights into PIF during GP residency. Doing the work of a GP exerted a pivotal influence on residents’ shift from doing as a GP to thinking, acting and feeling like a GP, that is, becoming a GP. Clinical supervisors are of utmost importance as role models and coaches in creating an environment that supports residents’ PIF. Implications for practice include faculty development initiatives to help supervisors be aware of how they can perform their various roles across different PIF stages.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".