Negotiating for Change. The Healthcare Manager as Catalyst for Evidence-Based Practice: Changing the Healthcare Environment and Sharing Experience
Bibliographic record
Abstract
This paper addresses the problem of the implementation of both clinical and managerial evidence-based decision-making in healthcare. The lack of implementation of research findings in clinical and management practice has been identified as a key failure in healthcare. Many await the development of better methods of research transfer led by academics and clinicians. Research transfer has become one of the highest priority are as for health services research. In this paper, the authors propose that the healthcare manager is well positioned to advance the research transfer process within the individual healthcare environment through two main mechanisms. First, healthcare managers can align decision-making structures within their own environment to facilitate evidence-based practice. This can occur by managers demonstrating a commitment to processes for the measurement and management of knowledge (identifying knowledge stewards) to the same extent that they demonstrate commitment to the measurement and management of finances. Within such a framework, the healthcare manager can build an environment in which there is an explicit "negotiation" between "knowledge stewards" (usually clinicians) and "financial stewards" (usually administrators) to achieve a common goal. The negotiation is an explicit, documented process that addresses the trade-offs that are made to avoid both financial and quality deficits in the organization (the quality deficit is defined as the gap between knowledge and practice). The second way in which healthcare managers can act as catalysts for promoting evidence-based practice is through cataloguing and reporting, using documented stories, the unique barriers to evidence-based approaches that are peculiar to their specific healthcare environments. It is hypothesized that local contextual circumstances, which can be expressed only through stories, are the most powerful barriers to research transfer within specific organizations. This has implications for what we count as useful knowledge as we try to better understand how rigorous research on the one hand and research and stories on the other contribute to strategies for research transfer at the organizational level.
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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 | 0.090 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.029 | 0.045 |
| Scholarly communication | 0.038 | 0.045 |
| Open science | 0.006 | 0.049 |
| Research integrity | 0.015 | 0.026 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".