Population-based integrated care funding values and guiding principles: An empirical qualitative study
Bibliographic record
Abstract
There is wide agreement on the benefits of integrated care; yet funding barriers persist. We suggest that funding models could currently hinder quality of care and that identifying values is necessary to designing adequate funding models. Yet it is currently unclear what are these values that ought to shape healthcare policy decisions. To fill in this gap, we conducted semi-structure interviews with fourteen health policy officials, managers, and researchers to elicit and explore how they conceptualize the values and guiding principles underlying these funding policies. Our findings suggest that values guide population-based integrated funding models, namely: accountability & integrity, transparency, equity, and innovation. Overall, funding mechanisms could incentivize integrated population-based care when the following conditions are met: a) there is transparent governance, with a whole-system approach, political will, and engagement and collaboration across health system partners, organizations and institutions, b) regulatory and evaluative frameworks support accountability including in decision-making, in outcomes and quality of care, as well as financial accountability; c) funding is equitable with a fair distribution of resources and supports accessibility to services; and d) funding mechanisms design and implementation include innovation enabling change, which are continuously evaluated. These values and guiding principles could be used in the development of funding models and future studies need to evaluate the effect of these values on decisions made by policy makers with respect to funding allocations and investments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".