Primary Care Data Reports in Ontario, Canada: Comparing primary care attachment rates from 2020 to 2022
Bibliographic record
Abstract
Context: Ontario Health Teams are made up of providers and organizations responsible for delivering health care to a defined population. Primary Care Data Reports provide key information about the population of patients attributed to each Ontario Health Team and are an important resource for applied health services researchers, health care managers and administrators, and health policy decision makers. Objective: Using standard health administrative measures in primary care in conjunction with measures for attachment to a primary care provider, Primary Care Data Reports were produced for regionally based Ontario Health Teams. Attachment categories include attached, uncertainly attached receiving primary care and uncertainly attached without primary care services. This study looks at the key differences that occurred between the first (March 31, 2020) and second version (March 31, 2022) of the reports. Study Design and Analysis: This cohort study used linked health administrative data sets in conjunction with measures of attachment to a primary care provider. Patient data is stratified according to key demographics, health care utilization and primary care indicators. Six priority populations of interest were produced by Ontario Health Team based on policy decision maker input. These priority populations included those who attended the emergency department, were hospitalized, received home care, had a mental health diagnosis, and who were in palliative care or had a frailty diagnosis. Dataset: Health administrative data sets in Ontario, Canada. Population studied: All residents of Ontario, Canada meeting study inclusion criteria. Instrument: Validated algorithm to assess patient attachment to primary care. Outcome measures: Attachment status, either as attached, uncertainly attached receiving primary care or uncertainly attached without primary care services. Results: There was a 2.5% drop in the attachment rate to a primary care provider from 2020 to 2022. Conclusions: Province wide changes in primary care attachment to a primary care provider worsened over the period of 2020-2022.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.023 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".