Who gets access to an interprofessional team-based primary care programme for patients with complex health and social needs? A cross-sectional analysis
Bibliographic record
Abstract
OBJECTIVES: To determine whether a voluntary referral-based interprofessional team-based primary care programme reached its target population and to assess the representativeness of referring primary care physicians. DESIGN: Cross-sectional analysis of administrative health data. SETTING: Ontario, Canada. INTERVENTION: TeamCare provides access to Community Health Centre services for patients of non-team physicians with complex health and social needs. PARTICIPANTS: All adult patients who participated in TeamCare between 1 April 2015 and 31 March 2017 (n=1148), and as comparators, all non-referred adult patients of the primary care providers who shared patients in TeamCare (n=546 989), and a 1% random sample of the adult Ontario population (n=117 753). RESULTS: TeamCare patients were more likely to live in lower income neighbourhoods with a higher degree of marginalisation relative to comparison groups. TeamCare patients had a higher mean number of diagnoses, higher prevalence of all chronic conditions and had more frequent encounters with the healthcare system in the year prior to participation. CONCLUSIONS: TeamCare reached a target population and fills an important gap in the Ontario primary care landscape, serving a population of patients with complex needs that did not previously have access to interprofessional team-based care. STRENGTHS AND LIMITATIONS: This study used population-level administrative health data. Data constraints limited the ability to identify patients referred to the programme but did not receive services, and data could not capture all relevant patient characteristics.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".