Can adaptive expertise, reflective practice, and activity theory help achieve systems-based practice and collective competence?
Bibliographic record
Abstract
Physicians must function as integral members of the complex social systems in which they work to support the health of their patients; competency-based education frameworks describe this function of physicians in terms of systems- based practice, advocacy, and collaboration. Yet education for these social competencies continues to present challenges, perhaps because medical education has tended to focus less on social systems and more on traditional healthcare systems. In this paper, we use a clinical example from the discipline of Developmental Pediatrics, that of early identification of autism spectrum disorder (ASD), as an illustration of a socially complex zone of practice necessitating systems-based practice. We first explore this practice context through the framings of collective competence and activity theory to represent the complex practices and systems involved in identifying ASD. We then align these framings of the practice context and complexity with two bodies of education theory, adaptive expertise and reflective practice. We argue that these approaches to education will prepare learners to be more aware of and responsive to the dynamic needs of the complex and intersecting systems in which they will practice.
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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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.068 |
| Scholarly communication | 0.011 | 0.020 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".