The development of self-efficacy beliefs during general practice vocational training: An exploratory study
Bibliographic record
Abstract
INTRODUCTION: Retention of general practitioners (GPs) is crucial to ensure appropriate primary care. However, some recently qualified GPs feel unprepared for practice, which may lead them to leave the profession or restrict their scope of practice. The development of self-efficacy beliefs during vocational training may be an important factor in this phenomenon. METHODS: Five focus groups with a total of 28 GP trainees and recent graduates were conducted in Belgium and France. Initial analysis using the immersion-crystallisation method was followed by analysis using Bandura's self-efficacy framework. RESULTS: Participants described beginning their training with low self-efficacy beliefs. Most participants described how they overcame stressful situations. Some, however, seemed to be developing avoidance strategies. Successfully resolving patient problems, sharing experiences with peers and receiving positive feedback from supervisors, colleagues and patients were conducive to the development of positive self-efficacy beliefs. DISCUSSION: Although low self-efficacy beliefs are natural at the beginning of training, participants seemed to develop in two ways, either overcoming their fears or avoiding them. Identifying the pattern of trainees' responses to allow tailoring of interventions should be investigated by those who run training programs. Interventions could include reassurance, peer interaction and an appropriate degree of autonomy.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".