Research Knowledge Translation in Sensory Integration-Based Therapy: Exploring Subjectivity of Clinical Expertise
Bibliographic record
Abstract
Background. Clinical expertise is the mechanism through which practitioners implement other components of evidence-based practice (EBP). Within occupational therapy practice, intervention approaches that are both closely and loosely aligned with Ayres’ Theory of Sensory Integration are widespread, offering a unique opportunity to investigate the subjective nature of clinical expertise in EBP. Purpose. This qualitative study explored motivations to offer sensory integration-based interventions, and factors informing occupational therapists’ clinical decision making in relation to an arguably contentious evidence base. Method. Six post-graduate sensory integration trained UK occupational therapists participated in individual semi-structured interviews. Interviews were transcribed, member-checked and analyzed using thematic coding analysis. Findings. Despite sound understanding of theory and continuous efforts to develop clinical knowledge, non-traditional hierarchies of evidence notably inform clinical decisions. The clinical expertise required for integration of patient preferences, clinical state and circumstances, and research evidence is informed by pragmatic responses to facilitators and barriers across contexts, combined with unique profession-specific identity factors. Implications. While empirical healthcare research is ideally undertaken under controlled conditions, realities of clinical practice are rarely so clear cut. Study findings highlight important subjective factors that are central to real-world research knowledge translation and further understanding of the clinical expertise component of EBP.
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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 | 0.266 | 0.303 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.007 | 0.052 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".