Organizational readiness for knowledge translation in chronic care: a review of theoretical components
Bibliographic record
Abstract
BACKGROUND: With the persistent gaps between research and practice in healthcare systems, knowledge translation (KT) has gained significance and importance. Also, in most industrialized countries, there is an increasing emphasis on managing chronic health conditions with the best available evidence. Yet, organizations aiming to improve chronic care (CC) require an adequate level of organizational readiness (OR) for KT. OBJECTIVES: The purpose of this study is to review and synthesize the existing evidence on conceptual models/frameworks of Organizational Readiness for Change (ORC) in healthcare as the basis for the development of a comprehensive framework of OR for KT in the context of CC. DATA SOURCES: We conducted a systematic review of the literature on OR for KT in CC using Pubmed, Embase, CINAHL, PsychINFO, Web of Sciences (SCI and SSCI), and others. Search terms included readiness; commitment and change; preparedness; willing to change; organization and administration; and health and social services. STUDY SELECTION: The search was limited to studies that had been published between the starting date of each bibliographic database (e.g., 1964 for PubMed) and November 1, 2012. Only papers that refer to a theory, a theoretical component from any framework or model on OR that were applicable to the healthcare domain were considered. We analyzed data using conceptual mapping. DATA EXTRACTION: Pairs of authors independently screened the published literature by reviewing their titles and abstracts. Then, the two same reviewers appraised the full text of each study independently. RESULTS: Overall, we found and synthesized 10 theories, theoretical models and conceptual frameworks relevant to ORC in healthcare described in 38 publications. We identified five core concepts, namely organizational dynamics, change process, innovation readiness, institutional readiness, and personal readiness. We extracted 17 dimensions and 59 sub-dimensions related to these 5 concepts. CONCLUSION: Our findings provide a useful overview for researchers interested in ORC and aims to create a consensus on the core theoretical components of ORC in general and of OR for KT in CC in particular. However, more work is needed to define and validate the core elements of a framework that could help to assess OR for KT in CC.
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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 | 0.030 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.024 | 0.027 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".