Combining adaptive expertise and (critically) reflective practice to support the development of knowledge, skill, and society
Bibliographic record
Abstract
Adaptive expertise (AE) and reflective practice (RP), two influential and resonant theories of professional expertise and practice in their own right, may further benefit health professions education if carefully combined. The current societal and systemic context is primed for both AE and RP. Both bodies of work position practitioners as agentive, learning continually and thoughtfully throughout their careers, particularly in order to manage unprecedented situations well. Similar on the surface, the roots and practices of AE and RP diverge at key junctures and we will focus on RP's movement toward critically reflective practice. The roots of AE and RP, and how they relate to or diverge from present-day applications matter because in health professions education, as in all education, paradigmatic mixing should be undertaken purposefully. This paper will explore the need for AE and RP, their shared commitments, distinctive histories, pedagogical possibilities both individually and combined, and next steps for maximizing their potential to positively impact the field. We argue that this exploration is urgently needed because both AE and RP hold much promise for improving health care and yet employing them optimally-whether alone or together-requires understanding and intent. We build an interprofessional education case situated in long-term care, throughout the paper, to demonstrate the potential that AE and RP might offer to health professions education individually and combined. This exploration comes just in time. Within the realities of uncertain practice emphasized by the pandemic, practitioners were also called to act in response to complex and urgent social movements. A combined AE and RP approach, with focus on critically reflective practice in particular, would potentially prepare professionals to respond effectively, compassionately, and equitably to future health and social crises and challenges.
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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 | 0.044 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.005 | 0.041 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".