Inter-provincial variation and determinants of access to team-based primary care in Canada
Bibliographic record
Abstract
Background: Team-based care involves family physicians working with other health professionals to provide primary care to patients. It has been implemented across Canada; however, its adoption varies, as health care delivery is the responsibility of provincial governments and not the federal government. Objective: To examine variations in the composition of team-based primary care amongst Canadian provinces in 2008 and identify patient characteristics that may have predicted access. Methods: Data are from the 2008 Canadian Survey of Experiences with Primary Health Care, a national survey of patients’ experiences with primary care in Canada. The sample size available for analysis was 11,521 and the response rate was 70.8%. Team-based care was defined as a family physician working with either a nurse or another type of health provider. Logistic regression was used to examine determinants of access to team-based care, adjusting for demographic, health status, and socioeconomic variables. Results: In 2008, 37.1% of Canadians reported having access to team-based care. The composition of team-based care varied amongst provinces and the most common model in all provinces were family physician plus nurse-only teams except in Quebec and Manitoba. Statistically significant predictors of access to team-based care were province of residence and total number of chronic conditions. Conclusion: With continuity of primary care reform in Canada, a new national survey is needed. Future assessments should aim to increase accuracy in the definition of team-based care through improvements in survey question design and patient education.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".