Improving Clarity and Interpretability of Items in a Bilingual Index of Propensity to Integrate Research Evidence into Clinical Decision-Making in Rehabilitation
Bibliographic record
Abstract
Abstract Purpose To contribute evidence for the clarity and interpretability of a new five-item bilingual multidimensional index of a rehabilitation clinician’s propensity to integrate research evidence into clinical decision-making. Methods This study was conducted in three sequential steps: (1) We conducted a focus group with occupational therapists, physical therapists, and researchers to review the items and response options for clarity, consistency, and interval properties and agree on equivalency in English and French. (2) We conducted cognitive interviews whereby clinicians elaborated on their interpretation of the item, comprehensibility of items, and appropriateness of response options. Accepted modifications were integrated and tested with subsequent participants. (3) We conducted an online survey to validate the English and French equivalency of response options on a 0 to 100 scale. Results During the qualitative revision process (one focus group with 7 participants followed by 27 interviews), the index was revised 12 times with substantial modifications to the use of research evidence and attitudes items. Conclusion This research increases the clinical relevance and reduces measurement error of this brief index which can inform on individual or organizational factors influencing a clinician’s propensity of integrating research evidence into decision-making and ultimately, improve rehabilitation outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.064 | 0.218 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".