Knowledge generation and utilisation in occupational therapy: Towards epistemic reflexivity
Bibliographic record
Abstract
AIM: The purpose of this article is to consider the ways in which theory generation, and hence knowledge generation, in occupational therapy is a complex social process, and therefore carries (often hidden) responsibilities for those who are part of our epistemic community. An epistemic community is a knowledge-producing community, who apply their standards of credibility, and epistemic values, to theory choice. In occupational therapy this community is comprised of a worldwide group of scholars and practitioners. METHODS: We propose that epistemic reflexivity can be used to critique and contribute to our disciplinary knowledge and to critically consider 'who' makes epistemological choices in our profession, and the consequent implications for the theories we adopt. The purpose of this article is to make these relations explicit so that scholars and therapists can become increasingly conscious and empowered with respect to their contributions to occupational therapy's epistemic community. To demonstrate an application of epistemic reflexivity, we critically consider a theoretical construction that has been widely adopted by the international occupational therapy community: evidence-based practice. RESULTS: As authors, we engage in epistemic reflexivity to critically consider the challenge posed by evidence-based practice. We propose a conception of practice knowledge that is informed by evidence yet based on a conception of wise practice. CONCLUSION: Our intention is to stimulate discussion and debate in occupational therapy's epistemic community, a number of approaches for fostering epistemic reflexivity in occupational therapy are suggested.
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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 | 0.174 | 0.178 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.011 | 0.119 |
| Scholarly communication | 0.030 | 0.040 |
| Open science | 0.005 | 0.034 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".