How core competencies are taught during clinical supervision: participatory action research in family medicine
Bibliographic record
Abstract
OBJECTIVES: The development of professional competence is the main goal of residency training. Clinical supervision is the most commonly used teaching and learning method for the development of core competencies (CCs). The literature provides little information on how to encourage the learning of CCs through supervision. We undertook an exploratory study to describe if and how CCs were addressed during supervision in a family medicine residency programme. METHODS: We selected a participatory action research design to engage participants in exploring their precepting practices. Eleven volunteer faculty staff and six residents from a large family medicine residency programme took part in a 9-month process which included three focus group encounters alternating with data gathering during supervision. We used mostly qualitative methods for data collection and analysis, with thematic content analysis, triangulation of sources and of researchers, and member checking. RESULTS: Participants realised that they addressed all CCs listed as programme outcomes during clinical supervision, albeit implicitly and intuitively, and often unconsciously and superficially. We identified a series of factors that influenced the discussion of CCs: (i) CCs must be both known and valued; (ii) discussion of CCs occurs in a constant adaptation to numerous contextual factors, such as residents' characteristics; (iii) the teaching and learning of CCs is influenced by six challenges in the preceptor-resident interaction, such as residents' active engagement, and (iv) coherence with other curricular elements contributes to learning about CCs. Differences between residents' and preceptors' perspectives are discussed. CONCLUSIONS: This is the first descriptive study focusing on the teaching of CCs during clinical supervision, as experienced in a family medicine residency programme. Content and process issues were equally influential on the discussion of CCs. Our findings led to a representation of factors determining the teaching and learning of CCs in supervision, and suggest directions for research, for faculty development, and for interventions with learners.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 teacher head, 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".