Educational Perspectives on Compassionate Concussion Care: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: Teaching compassionate concussion care, particularly by engaging patients and caregivers as partners in education, is a complex and evolving field. Clinician-educators are now expected to move beyond traditional methods and draw on diverse approaches to understand how people learn. Yet, many current teaching practices lack clear theoretical grounding, limiting their ability to prepare physicians to address patients' individual needs. Despite growing interest in compassion education, little is known about how paradigms shape postgraduate concussion-care training and assessment. This scoping review aimed to (1) explore the educational paradigms and learning theories underpinning postgraduate concussion-care education and (2) contrast the paradigms guiding assessment of and for learning. METHODS: Following Arksey and O'Malley's scoping review framework, we searched MEDLINE, Embase, ERIC, Cochrane, and CINAHL. Eligible articles described full-length postgraduate concussion-care educational interventions. Extracted data included intervention design, educational paradigm, learning theory and reported outcomes. FINDINGS: Of the 1574 articles screened, 9 met inclusion criteria. Identified paradigms included behaviourism, positivism, cognitivism and constructivism. Social-cultural learning theory (a form of Constructivism) appeared in six of nine studies. Most studies did not explicitly state their guiding paradigm or align assessment with compassionate outcomes. CONCLUSION: This review highlights the implicit paradigms shaping concussion-care education and their limitations for cultivating compassion. Constructivism offers the most promise for advancing compassionate practice by fostering collaboration, reflection, and learner agency. Given the interpersonal, cognitive and contextual demands of concussion care, adopting a constructivist orientation may better prepare physicians to meet patient and caregiver needs.
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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 | 0.013 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.024 | 0.024 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".