Development and Content Validation of a Transcultural Instrument to Assess Organizational Readiness for Knowledge Translation in Healthcare Organizations: The OR4KT
Bibliographic record
Abstract
BACKGROUND: Implementing effective interventions in healthcare requires organizations to be ready to support change. This study aimed to develop, adapt transculturally, and assess the content and face validity of the Organizational Readiness for Knowledge Translation (OR4KT) tool. The OR4KT was designed to measure the readiness of healthcare organizations to implement evidence-informed change across a variety of services. METHODS: Based on systematic reviews of the literature, a Delphi exercise, and expert consultation, we first generated an initial pool of items. Second, we developed and assessed content validity of the pilot OR4KT questionnaire in English. Third, we created French and Spanish versions using a sequential forward and backward translation approach, and transcultural adaptation by a consensus process. Finally, we conducted pilot studies in three contexts - the Basque country region (Spain), and the provinces of Québec and Ontario (Canada) - where 30 experts assessed the face validity of the three versions of OR4KT. RESULTS: We selected 59 items, grouped in 6 dimensions (organizational climate, context, change content, leadership, organizational support, and motivation) for the final English version of OR4KT. Translation and transcultural adaptation did not identify any content or language problems. Our findings indicate that the English, French and Spanish versions of OR4KT are linguistically equivalents and have high face validity. Only minor revisions to the wording of some items were recommended. CONCLUSION: The OR4KT holds promise as a measure of readiness for knowledge translation (KT) in healthcare organizations. The validity and reliability of the three versions of the OR4KT will be assessed in real-life contexts of implementation of evidence-based changes in healthcare.
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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 | 0.052 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".