Mining the Management Literature for Insights into Implementing Evidence-Based Change in Healthcare
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.
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 | 0.130 | 0.410 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.116 | 0.068 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.019 | 0.019 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| 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".