Challenges in measuring integrated care models: International knowledge and the case of Québec
Bibliographic record
Abstract
Introduction The implementation of integrated care models requires significant efforts, especially due to institutional and organisational inertial forces that characterize health and social care systems of developed countries. It is therefore crucial to deploy strategies that promote continuous adjustment to these barriers so as to improve the benefits of integrating care. Measuring the implementation and effects of integrated care models are key component of these strategies. However, measuring integrated care also faces major challenges. This study aims to identify and characterise integrated care measurement challenges. Methods A review of reviews on the measurement of integrated care identified 12 papers. A thematic analysis was conducted to identify and categorize measurement challenges. Document analysis was done on the measurement of an integrated care model for older adults in Québec. Results Eight categories of measurement challenges were identified. These challenges include difficulties in measuring structures, processes, and effects of models; conceptual ambiguity and heterogeneity of organisational forms; involving multiple actors in the measurement strategy; and including multiple data sources, amongst others. These challenges revealed and explained potential gaps in the measurement of integrated care for older adults in Québec. For instance, the Québec measurement strategy did not include effects indicators. Conclusion Although the measurement of integrated care is a complex endeavour, there is a need for adequate measurement strategies that allow to appreciate important elements of integrate care. The findings of this study could be used as a reflexive tool in advancing research and practice of measuring integrated care.
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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 | 0.033 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".