Trends in attachment to a primary care provider in Ontario, 2008–2018: an interrupted time-series analysis
Bibliographic record
Abstract
<h3>Background:</h3> Attachment to a regular primary care provider is associated with better health outcomes, but 15% of people in Canada lack a consistent source of ongoing primary care. We sought to evaluate trends in attachment to a primary care provider in Ontario in 2008–2018, through an equity lens and in relation to policy changes in implementation of payment reforms and team-based care. <h3>Methods:</h3> Using linked, population-level administrative data, we conducted a retrospective observational study to calculate rates of patients attached to a regular primary care provider from Apr. 1, 2008, to Mar. 31, 2019. We evaluated the association of patient characteristics and attachment in 2018 using sex-stratified, adjusted, multivariable logistic regression models and used segmented piecewise regression to evaluate changing trends before and after implementation of a policy that restricted physician entry to alternate models. <h3>Results:</h3> Attachment increased from 80.5% (<i>n</i> = 10 352 385) in 2008 to 88.9% of the population (<i>n</i> = 12 537 172) in 2018, but was lower among people with low comorbidity, high residential instability, material deprivation, rural residence and recent immigrants. Inequities narrowed for recent immigrants, males and people with lower incomes over the study period, but disparities persisted for these groups. Attachment grew by 1.47% annually until 2014 (<i>p</i> < 0.0001), but was stagnant thereafter (annual percent change of 0.13, <i>p</i> = 0.16). <h3>Interpretation:</h3> Lack of sustained progress in attachment followed reduced levels of physician entry to alternate funding models. Although disparities narrowed for many groups over the study period, persistent gaps remained for immigrants and people with lower incomes; targeted interventions and policy changes are needed to address these persistent gaps.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; both teacher heads agree on what is shown here.
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".