Bibliographic record
Abstract
Browman, Snider and Ellis propose that managers act as catalysts to institute structures for the negotiation of differences between managers and clinicians, primarily around the perceived trade-offs between quality care and resource allocation. Evidence-based medicine and evidence-based management are required to level the playing field, so that quality of care will not suffer "at the expense of the business agenda which is easier to measure and manage." But implementation of evidence-based management and evidence-based medicine depends on the quality of knowledge production and the ability to adapt general knowledge to particular organizations. Agreement on guidelines for evidence-based management is problematic and complicated by the nature of management research itself. Rather than the metaphor of the negotiating environment, which assumes an adversarial relationship between management and clinicians, a more fruitful metaphor is the expert environment, which assumes a collaborative relationship between the two. Management should not only be a catalyst for change but should also assume the responsibility of cultivating clinical and managerial expertise at all levels of the organization, from custodian to surgeon.
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 | 0.014 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.050 |
| Scholarly communication | 0.011 | 0.025 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.026 | 0.046 |
| Insufficient payload (model declined to judge) | 0.007 | 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".