Funding and remuneration of interdisciplinary primary care teams in Canada: a conceptual framework and application
Bibliographic record
Abstract
BACKGROUND: Reliance on interdisciplinary teams in the delivery of primary care is on the rise. Funding bodies strive to design financial environments that support collaboration between providers. At present, the design of financial arrangements has been fragmented and not based on evidence. The root of the problem is a lack of systematic evidence demonstrating the superiority of any particular financial arrangement, or a solid understanding of options. In this study we develop a framework for the conceptualization and analysis of financial arrangements in interdisciplinary primary care teams. METHODS: We use qualitative data from three sources: (i) interviews with 19 primary care decision makers representing 215 clinics in three Canadian provinces, (ii) a research roundtable with 14 primary care decision makers and/or researchers, and (iii) policy documents. Transcripts from interviews and the roundtable were coded thematically and a framework synthesis approach was applied. RESULTS: Our conceptual framework differentiates between team level funding and provider level remuneration, and characterizes the interplay and consonance between them. Particularly the notions of hierarchy, segregation, and dependence of provider incomes, and the link between funding and team activities are introduced as new clarifying concepts, and their implications explored. The framework is applied to the analysis of collaboration incentives, which appear strongest when provider incomes are interdependent, funding is linked to the team as a whole, and accountability does not have multiple lines. Emergent implementation issues discussed by respondents include: (i) centrality of budget negotiations; (ii) approaches to patient rostering; (iii) unclear funding sources for space and equipment; and (iv) challenges with community engagement. The creation of patient rosters is perceived as a surprisingly contentious issue, and the challenges of funding for space and equipment remain unresolved. CONCLUSIONS: The development and application of a conceptual framework is an important step to the systematic study of the best performing financial models in the context of interdisciplinary primary care. The identification of optimal financial arrangements must be contextualized in terms of feasibility and the implementation environment. In general, financial hierarchy, both overt and covert, is considered a barrier to collaboration.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.020 | 0.017 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".