Mining the Management Literature for Insights into Implementing Evidence-Based Change in Healthcare
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: We synthesized the management and health literatures for insights into implementing evidence-based change in healthcare drawn from industry-specific data. Because change principles based on evidence often fail to be translated into organizational practice or policy, we sought studies at the nexus of organizational change and knowledge translation. METHODS: We reviewed five top management journals to identify an initial pool of 3,091 studies, which yielded a final sample of 100 studies. Data were abstracted, verified by the original authors and revised before entry into a database. We employed a systematic narrative synthesis approach using words and text to distill data and explain relationships. We categorized studies by varying levels of relevance for knowledge translation as (1) primary, direct; (2) intermediate; and (3) secondary, indirect. We also identified recurring categories of change-related organizational factors. The current analysis examines these factors in studies of primary relevance to knowledge translation, which we also coded for intervention readiness to reflect how readily change can be implemented. Preliminary RESULTS AND CONCLUSIONS: Results centred on five change-related categories: Tailoring the Intervention Message; Institutional Links/Social Networks; Training; Quality of Work Relationships; and Fit to Organization. In particular, networks across institutional and individual levels appeared as prominent pathways for changing healthcare organizations. Power dynamics, positive social relations and team structures also played key roles in implementing change and translating it into practice. We analyzed journals in which first authors of these studies typically publish, and found evidence that management and health sciences remain divided. Bridging these disciplines through research syntheses promises a wealth of evidence and insights, well worth mining in the search for change that works in healthcare transformation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it