The impact of acute care clinical practice guidelines on length of stay: A closer look at some conflicting findings
Bibliographic record
Abstract
Systematic reviews have found that clinical practice guidelines (CPGs) are associated with lower lengths of stay (LOS), but a secondary analysis of Ontario acute care hospitals found few significant relationships between CPGs and LOS. This research explored possible reasons for these findings and what other factors may impact the CPG-LOS relationship. Semi-structured interviews were conducted with staff from nine hospitals whose jobs dealt with developing, implementing, monitoring, updating, or evaluating CPGs. Interviews were analyzed utilizing methods outlined by Aurebach. A variety of leaders and hospital types were represented. Five main factors influencing relationships between CPGs and LOS were identified: 1) the purpose of implementation, 2) evidence base for CPG content and selection, 3) health care professionals’ response to change and compliance, 4) dissemination strategies, and 5) organizational support and resources. The interviews suggested possible reasons why CPGs are not realizing their full potential impact on LOS in Ontario hospitals, ranging from poor compliance to resistance from health care providers. CPGs themselves are not perceived to be the reason for ineffectiveness; rather, organizational- and individual-level barriers seem to be the causes.
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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 | 0.145 | 0.482 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".