Development of a program theory for clinical pathways in hospitals: protocol for a realist review
Bibliographic record
Abstract
BACKGROUND: Despite the increased utilization of clinical pathways (CPWs) as a strategy to improve patient and system outcomes in hospitals, there remain ongoing challenges with their conceptualization, implementation, and evaluation. Theories that explain how CPWs work in hospitals are lacking, making it difficult to identify important factors for sustaining changes arising from CPWs implemented in hospitals. The objective of this realist review is to develop a program theory for CPWs in hospitals. METHODS: This is a protocol for a realist review. The review will use a six-step iterative process to develop a program theory for CPWs in hospitals: (1) development of a preliminary program theory; (2) search strategy and literature search; (3) study selection and appraisal; (4) data extraction; (5) data analysis and synthesis; and (6) stakeholder engagement. In addition to searching the gray literature and contacting authors, we will search electronic databases such as MEDLINE, NHSEED, CINAHL EBSCO, HMIC, and PsycINFO. Studies will be included based on their ability to provide data that test some aspect of the program theory. Two independent reviewers will select, screen, and extract data related to the program theory from all relevant sources. A realist logic of analysis will be used to identify all context-mechanism-outcome heuristics that explains how CPWs implemented in hospitals translates to better health system outcomes. DISCUSSION: Overall, the review aims to develop a program theory for CPWs in hospitals and to explore how, why, to what extent, and in what contexts does the implementation of CPWs in hospitals contribute to better health system outcomes. As a result, the review will provide a theoretical framework of how CPWs work in hospitals. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42018103220.
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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 | 0.189 | 0.254 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.013 | 0.015 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.009 | 0.009 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.068 | 0.014 |
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".