Interprofessional Teams in the Context of Primary Care Reform in Ontario, Canada: Selection Factors and Association with Access to Care and Health Services Utilization
Bibliographic record
Abstract
Background: Countries throughout the world have been exploring new models to deliver primary care. Ontario has undergone a primary care reform that includes the introduction of interprofessional teams. The purpose of this thesis was to investigate the association between receiving care from interprofessional versus non-interprofessional primary care teams and access to care and health services utilization. The first study investigated selection factors into interprofessional teams. The second and third studies compared interprofessional teams and non-interprofessional teams on access and health services utilization measures.Methods: The three studies linked provincial administrative datasets (second study included a provincial healthcare experience survey as well) to assess outcomes of interest over time. The first study was cross-sectional and the last two were retrospective cohort studies. Results: The first study identified that there are selection factors into interprofessional teams. The second study findings highlighted that as compared to Health Care Experience Survey respondents in non-interprofessional teams, respondents in interprofessional teams self-reported more timely access to care and less walk-in clinic use but no significant difference in self-reported access to after-hours care and emergency department use. The third study found that there was no difference in the change over time in Ambulatory Care Sensitive Conditions admissions and all cause hospital re-admission between interprofessional and non-interprofessional teams between the pre- and post-implementation periods. Conclusion: Ontario has made a major investment in interprofessional team-based care. The findings from this thesis indicate that there are selection factors into interprofessional teams. Interprofessional teams perform better than non-interprofessional teams on some but not all investigated processes and outcomes of care. Our findings can inform other jurisdictions aiming to expand voluntary participation in interprofessional primary care teams regarding expectations about the relationship between primary care policy, organization and delivery and patient experience and health services utilization.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".