The Healthcare Manager as Catalyst for Evidence-Based Practice: Changing the Healthcare Environment and Changing Experience
Bibliographic record
Abstract
This thoughtful essay conveys a logically ordered series of concepts that may help develop a process to more fully integrate evidence into healthcare management decisions. In responding to this article we have tried to think broadly about what might be discovered if the ideas of Browman, Snider and Ellis were put into practice. We have chosen to focus on factors that could confound the five-point plan suggested by the authors. Browman, Snider and Ellis provide an interesting overview and advocacy for evidence-based medicine and its application. The authors recognize that the area under investigation, oncology, may lend itself more readily than others to the facilitation of evidence-based practice. They introduce the concept of healthcare as a negotiating environment and the healthcare manager as a potential catalyst for change. In their conclusions the authors offer healthcare managers a five-point plan: 1. Express the value that knowledge is a core resource. 2. Structure decision-making to involve the stewards of knowledge and stimulate negotiation while encouraging the parties to express shared goals. 3. Invest in strategies that support knowledge management, including arbitration. 4. Demonstrate leadership and develop organizational metaphors to encourage participation. 5. Share the results and experiences of evidence-based decision-making.
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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 | 0.015 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.048 | 0.058 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".