Organizational readiness for knowledge translation in chronic care: a Delphi study
Bibliographic record
Abstract
BACKGROUND: Health-care organizations need to be ready prior to implement evidence-based interventions. In this study, we sought to achieve consensus on a framework to assess the readiness of health-care organizations to implement evidence-based interventions in the context of chronic care. METHODS: We conducted a web-based modified Delphi study between March and May 2013. We contacted 76 potentially eligible international experts working in the fields of organizational readiness (OR), knowledge translation (KT), and chronic care to comment upon the 76 elements resulting from our proposed conceptual map. This conceptual map was based on a systematic review of the existing frameworks of Organizational Readiness for Change (ORC) in health-care. We developed a conceptual map that proposed a set of core concepts and their associated 17 dimensions and 59 sub-dimensions. Experts rated their agreement concerning the applicability and importance of ORC elements on a 5-point Likert scale, where 1 indicates total disagreement and 5 indicates total agreement. Two rounds were needed to get a consensus from the experts. Consensus was a priori defined as strong (≥75%) or moderate (60-74%). Simple descriptive statistics was used. RESULTS: In total, 14 participants completed the first round and 10 completed the two rounds. Panel members reached consensus on the applicability and importance of 6 out of 17 dimensions and 28 out of 59 sub-dimensions to assess OR for KT in the context of chronic care. A strong level of consensus (≥75%) was attained on the Organizational contextual factors, Leadership/participation, Organizational support, and Motivation dimensions. The Organizational climate for change and Change content dimensions reached a moderate consensus (60-74%). Experts also reached consensus on 28 out of 59 sub-dimensions to assess OR for KT. Twenty-one sub-dimensions reached a strong consensus (≥75%) and seven a moderate consensus (60-74%). CONCLUSION: This study results provided the most important and applicable dimensions and sub-dimensions for assessing OR-KT in the context of chronic care. They can be used to guide the design of an assessment tool to improve knowledge translation in the field of 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 | 0.100 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".