Examining Health Care Managers’ Use of Knowledge: A Review and Synthesis
Bibliographic record
Abstract
Background. Despite acceptance of the merits of evidence-based practice, health care managers are cited as discounting research evidence to inform management practice. The purpose of this review was to evaluate the effectiveness of interventions to enhance health care managers’ use of research in their management practice. Methods. We searched ten online bibliographic databases. Articles eligible for inclusion reported on interventions targeting health care managers to enhance research utilization in their practice. Reviewers independently screened abstracts and manuscripts using predefined inclusion criteria. We employed Hoon’s (2013) approach to meta-synthesis of qualitative studies to synthesize review results. Results. Seven primarily qualitative studies of variable quality (reported in 11 articles) met inclusion criteria. Interventions to enhance health care managers’ research use included: informal/formal training, a computer-based/desktop application; meeting based, executive- level knowledge translation activities; and formal residency programs. Meta-synthesis yielded four themes including organizational culture/context, prioritization, time as a resource and capacity building. Conclusions. Qualitative results can inform future studies, with study designs that can examine the relative effectiveness of specific components of an intervention in this area. The small number of studies available in the literature and the diverse strategies employed hindered our ability to identify one intervention as superior to any other.
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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 | 0.034 | 0.117 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.027 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".