Measuring Access to and Quality of Primary Care in Quebec: Insights from Research on Patient Enrolment Policies
Bibliographic record
Abstract
More than one million “orphan” patients do not have a family physician in Quebec as revealed by the department of Health and Social Services scoreboard. This spring, Minister Dubé has launched several initiatives aimed at transforming the health system to facilitate access to high-quality and timely primary care services. These reforms focus primarily on the enrolment of patients with a family physician. Having access to a regular source of care is almost universally seen as a good thing. In this short note, Erin C. Strumpf, McGill University Professor and Fellow CIRANO, and her co-authors challenge this idea. They show that there is a tendency to confuse concepts and assume that repeated contact is evidence of a truly caring, trusting patient-physician relationship, which could ultimately lead to better health outcomes. If we want to effectively create and evaluate interventions aimed at improving primary care, it is essential to clearly identify the processes through which patient care can be improved and to identify the most relevant measures that actually capture the outcomes of interest such as affiliation and continuity of care. That is what the authors precisely do here. By being honest and clear about what we can actually measure and evaluate with the data we have, they argue that we create an opening for more creative approaches to health policy evaluation.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".