Indicators and measurement tools for health system integration: a knowledge synthesis protocol
Bibliographic record
Abstract
Background: Health system integration is a key component of health system reform with the goal of improving outcomes for patients, providers, and the health system. Although health systems continue to strive for better integration, current delivery of health services continues to be fragmented. A key gap in the literature is the lack of information on what successful integration looks like and how to measure achievement towards an integrated system. This multi-site study protocol builds on a prior knowledge synthesis completed by two of the primary investigators which identified 10 key principles that collectively support health system integration. The aim is to answer two research questions: What are appropriate indicators for each of the 10 key integration principles developed in our previous knowledge synthesis and what measurement tools are used to measure these indicators? To enhance generalizability of the findings, a partnership between Canada and Brazil was created as health system integration is a priority in both countries and they share similar contexts. Methods/design: This knowledge synthesis will follow an iterative scoping review process with emerging information from knowledge-user engagement leading to the refinement of research questions and study selection. This paper describes the methods for each phase of the study. Research questions were developed with stakeholder input. Indicator identification and prioritization will utilize a modified Delphi method and patient/user focus groups. Based on priority indicators, a search of the literature will be completed and studies screened for inclusion. Quality appraisal of relevant studies will be completed prior to data extraction. Results will be used to develop recommendations and key messages to be presented through integrated and end-of-grant knowledge translation strategies with researchers and knowledge-users from the three jurisdictions. Discussion: This project will directly benefit policy and decision-makers by providing an easy accessible set of indicators and tools to measure health system integration across different contexts and cultures. Being able to evaluate the success of integration strategies and initiatives will lead to better health system design and improved health outcomes for patients.
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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 | 0.219 | 0.233 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.062 | 0.011 |
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".