Development of an organizational typology of interprofessional primary care teams in Quebec, Canada: A multivariate analysis
Bibliographic record
Abstract
BACKGROUND: This study aimed to develop an organizational typology of Interprofessional Primary Care (IPC) teams in Quebec, Canada, by describing their organizational profiles and assessing the association between the characteristics of the populations served and the organizational profiles. METHODS: This cross-sectional study was carried out using a finite mixture model of the 2021 financial monitoring data from the Ministry of Health and Social Services of Quebec. The population consisted of all IPC teams in Quebec (N = 368). A multinomial logistic model was used to assess the association between the population characteristics and the organizational profiles. RESULTS: The analysis revealed that IPC teams were heterogeneous and could be classified into five distinct profiles varying in size, team composition, sector, type, and level of partnership. Pregnant women (odds ratio [OR] = 2.78, 95 % confidence interval [CI] 1.98-3.91), disadvantaged patients ([OR] = 1.62, [CI] 1.15-2.28), patients receiving homecare support ([OR] = 1.85, [CI] 1.28-2.66) and rural patients ([OR] = 0.66, [CI] 0.50-0.86)) were more likely to be associated to the medium, public, university-affiliated, practitioner-oriented, low partnered profile compared to the very small, private, regular, high-partnered profile. CONCLUSION: IPC teams can be characterized into five distinct profiles that are associated with the characteristics of the populations they serve. These results may help to better evaluate if the desired effects of IPC teams have been achieved.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".