Learning to lead: a review and synthesis of literature examining health care managers' use of knowledge
Bibliographic record
Abstract
BACKGROUND: Scholarship cites health care managers (HCMs) as not using research evidence in their management practice. The purpose of this review was to evaluate the effectiveness of interventions to enhance HCMs use of research evidence in practice. METHODS: We carried out a systematic review and focus groups to validate the review findings. We searched 10 electronic databases for studies reporting on interventions for HCMs to enhance research utilization in their practice. Qualitative studies were analysed using Hoon's approach to meta-synthesis. RESULTS: Seven, primarily qualitative, studies of varying quality (reported in 11 articles) met our inclusion criteria. Interventions to enhance research use by HCMs included: informal and formal training, computer-based application, executive-level knowledge translation activities and residency programmes. Studies did not report efficacy of interventions or impacts of increasing managers' use of research on staff or patient outcomes. Meta-synthesis yielded four contextual factors influencing the perceived effectiveness of interventions to enhance research use by HCMs: organizational culture, competing priorities, time as a resource and capacity building. Included studies differed in how they defined research and demonstrated varying understandings of research among HCMs, limiting the generalizability of work in this field. CONCLUSIONS: Healthcare managers are increasingly called upon to make evidence-based decisions in practice, but the small number of studies and diverse strategies employed hinder our ability to identify any intervention to increase use of evidence as superior. Future studies in this area should clearly articulate the definition of research evidence they base their decisions on. Registration: PROSPERO (CRD42014006256).
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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 | 0.040 | 0.140 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.043 | 0.033 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".