Toward an inclusive evidence-based practice model: Embracing a broader conception of professional knowledge in health care and health care higher education
Bibliographic record
Abstract
Evidence-based practice (EBP) and the evidence-based practice model (EBPM) are currently taken for granted as a guide for teaching and learning ‘best practice’ in higher education health care programs. As health care educators and researchers, we argue for enhancement of the model by inclusion of a broader conception of professional knowledge, including ethical care. In this conceptual paper, we draw on hermeneutic inquiry to reflect on theoretical underpinnings informing earlier discussions of EBP and the EBPM. Also, we enhance our critical thinking by turning to Aristotle. Taken together our reflections bring to the fore an awareness of conflicting logics embedded in the EBPM. We contend that an Aristotelian understanding, however, allows professional knowledge to be reinvigorated by bolstering possibilities for pluralistic conceptions of knowledge. In conclusion, we propose an elaborated EBPM termed the inclusive EBPM. The model includes ethical care as a to guide to teaching and learning of ‘best 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 | 0.104 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.010 | 0.121 |
| Scholarly communication | 0.033 | 0.039 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.018 | 0.020 |
| Insufficient payload (model declined to judge) | 0.002 | 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".