A Systematic Review of Instruments to Assess Organizational Readiness for Knowledge Translation in Health Care
Bibliographic record
Abstract
BACKGROUND: The translation of research into practices has been incomplete. Organizational readiness for change (ORC) is a potential facilitator of effective knowledge translation (KT). However we know little about the best way to assess ORC. Therefore, we sought to systematically review ORC measurement instruments. METHODS: We searched for published studies in bibliographic databases (Pubmed, Embase, CINAHL, PsychINFO, Web of Science, etc.) up to November 1st, 2012. We included publications that developed ORC measures and/or empirically assessed ORC using an instrument at the organizational level in the health care context. We excluded articles if they did not refer specifically to ORC, did not concern the health care domain or were limited to individual-level change readiness. We focused on identifying the psychometric properties of instruments that were developed to assess readiness in an organization prior to implementing KT interventions in health care. We used the Standards for Educational and Psychological Testing to assess the psychometric properties of identified ORC measurement instruments. FINDINGS: We found 26 eligible instruments described in 39 publications. According to the Standards for Educational and Psychological Testing, 18 (69%) of a total of 26 measurement instruments presented both validity and reliability criteria. The Texas Christian University -ORC (TCU-ORC) scale reported the highest instrument validity with a score of 4 out of 4. Only one instrument, namely the Modified Texas Christian University - Director version (TCU-ORC-D), reported a reliability score of 2 out of 3. No information was provided regarding the reliability and validity of five (19%) instruments. CONCLUSION: Our findings indicate that there are few valid and reliable ORC measurement instruments that could be applied to KT in the health care sector. The TCU-ORC instrument presents the best evidence in terms of validity testing. Future studies using this instrument could provide more knowledge on its relevance to diverse clinical contexts.
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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 | 0.029 | 0.135 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.029 | 0.028 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".