Selection of Priority Population and Health Outcome Performance Indicators by Integrated Healthcare Networks: An Exploratory Multiple-Case Study
Bibliographic record
Abstract
Background: Integrated care requires shared sense-making and decision-making processes among stakeholders, which are often affected by empirical evidence, and interactions and relationships between stakeholders. This dissertation explores the selection processes of priority population and performance indicators of OHTs, while identifying factors influencing them. Method: The study followed an exploratory multiple-case study design with theoretical replication. Two cases were selected based on set criteria; 12 interviews were conducted (six each) with the decision-making committee members. Data were analyzed using a case-based approach to cross-case synthesis. Results: Findings showed that sense-making and decision-making of OHTs were intricately connected and were not always governed by data. Organizational expertise, stakeholder interest, previous experience, and feasibility often guided the understanding of health system issues, thus influencing population and indicator prioritization. Conclusion: Different sense-making processes may lead to variations in decisions across integrated care networks, adapting to individual contexts.
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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 | 0.023 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".