Organizational Typology of Interprofessional Primary Care Teams in Quebec
Bibliographic record
Abstract
Context Interprofessional primary care teams (IPCT) are the main model for organizing primary health care. To date, little is known about the organizational heterogeneity of IPCT and how this heterogeneity may be related to the characteristics of the population they serve. Objective The purpose of this study was to describe the current profiles of IPCT. Setting and population studied 369 IPCT in Quebec, Canada. Study Design and analysis This cross-sectional study performed a mixture model analysis to identify the underlying profiles of the 369 IPCT. The data source was the Financial Monitoring file of the Health Ministry of Quebec. The organizational characteristics included in the model were the number of full-time equivalents (FTEs) of physicians, nurse practitioners, registered nurses, and social workers, type of IPCT (network, university-affiliated, regular), sector (private, public, mixed), presence of a pharmacist, presence of a partner agreement (with another IPCT or a hospital), number of registered patients, attendance rate, number of practices affiliated under the same IPCT and total funding. The optimal number of profiles was determined by statistical criteria (AIC, BIC) and clinical significance. Outcome measures The outcome measure was the organizational profiles of IPCT. Results Six profiles of IPCT were identified. The first profile (20%) included high budget, private IPCT that favoured non-physicians in their teams. The second profile (9%) were low budget, private IPCT with low registration rates. The third profile (7%) described average to high budget, private IPCT that favoured physicians in their teams. Fourth profile (44%) included low to average budget, private IPCT that favoured non-physicians in their teams, and low registration rates. The fifth profile (4%) was a high budget, public and university typed IPCT that favoured physicians in their team and with average registration rates. The last profile (17%) was a high budget, public and university typed IPCT, that favoured physicians in their teams and with low registration rates. Expected outcomes The organizational profiles of IPCT helped determine the heterogeneity of this primary care model. The identification of the heterogeneity will help with the design of organizational and funding policies that will enhance the impact of the IPCT in health outcomes. Further research will help to understand whether this model meets the needs of the population it deserves.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".