Perspectives on expert generalist practice among Japanese family doctor educators: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Expert generalist practice (EGP) is increasingly being viewed as the defining expertise of generalist care. In Japan, several prominent family doctors consider it important and relevant in the Japanese context. However, no study has examined Japanese family doctor educators' perceptions of EGP. AIM: To explore Japanese family doctor educators' perceptions of EGP. DESIGN & SETTING: A qualitative study among family doctor educators in Japan. METHOD: Focus group interviews were conducted using a semi-structured interview guide following a short lecture on EGP. A qualitative description method was adopted and the framework method was used to conduct thematic analysis. RESULTS: Participants were 18 family medicine doctor educators, including 11 directors and six associate directors of family medicine training programmes. The results suggested that the concept of EGP was important and applicable to primary care in Japan. Participants' perceptions on EGP pertained to the following four areas: impact of EGP, triggers for EGP, enablers for EGP, and educational strategies for EGP. CONCLUSION: The concept of EGP may be useful in clinical practice in Japan, especially in complex patient care. A clearer framework for or description of EGP, and of non-traditional methods, such as ascetic practice and awareness of the self, were proposed as possible educational strategies.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".