Measuring organizational readiness for knowledge translation in chronic care
Bibliographic record
Abstract
BACKGROUND: Knowledge translation (KT) is an imperative in order to implement research-based and contextualized practices that can answer the numerous challenges of complex health problems. The Chronic Care Model (CCM) provides a conceptual framework to guide the implementation process in chronic care. Yet, organizations aiming to improve chronic care require an adequate level of organizational readiness (OR) for KT. Available instruments on organizational readiness for change (ORC) have shown limited validity, and are not tailored or adapted to specific phases of the knowledge-to-action (KTA) process. We aim to develop an evidence-based, comprehensive, and valid instrument to measure OR for KT in healthcare. The OR for KT instrument will be based on core concepts retrieved from existing literature and validated by a Delphi study. We will specifically test the instrument in chronic care that is of an increasing importance for the health system. METHODS: Phase one: We will conduct a systematic review of the theories and instruments assessing ORC in healthcare. The retained theoretical information will be synthesized in a conceptual map. A bibliography and database of ORC instruments will be prepared after appraisal of their psychometric properties according to the standards for educational and psychological testing. An online Delphi study will be carried out among decision makers and knowledge users across Canada to assess the importance of these concepts and measures at different steps in the KTA process in chronic care.Phase two: A final OR for KT instrument will be developed and validated both in French and in English and tested in chronic disease management to measure OR for KT regarding the adoption of comprehensive, patient-centered, and system-based CCMs. DISCUSSION: This study provides a comprehensive synthesis of current knowledge on explanatory models and instruments assessing OR for KT. Moreover, this project aims to create more consensus on the theoretical underpinnings and the instrumentation of OR for KT in chronic care. The final product--a comprehensive and valid OR for KT instrument--will provide the chronic care settings with an instrument to assess their readiness to implement evidence-based chronic care.
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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 | 0.076 | 0.135 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".