Evaluation of the implementation of centralized waiting lists for patients without a family physician and their effects across the province of Quebec
Bibliographic record
Abstract
BACKGROUND: Most national and provincial commissions on healthcare services in Canada over the past decade have recommended that primary care services be strengthened in order to guarantee each citizen access to a family physician. Despite these recommendations, finding a family physician continues to be problematic. The issue of enrollment with a family physician is worrying in Canada, where nearly 21% of the country's population reported not having a family physician in the last Commonwealth Fund survey.To respond to this important need, centralized waiting lists have been implemented in four Canadian provinces to help 'orphan,' or unaffiliated, patients find a family physician. These organizational mechanisms are intended to better coordinate the demand for and supply of family physicians. The objectives of this study are: to assess the effects of centralized waiting lists for orphan patients (GACOs) implemented in the province of Quebec and to explain the variation among their effects by analyzing factors influencing implementation process. METHODS: This study is based on two complementary and sequential research strategies. The first (objective 1) is a quantitative longitudinal design to assess the effects of all the GACOs (n = 93) in Quebec using clinical-administrative data. The second (objective 2) involves using four case studies to explain variations in effects through in-depth analysis of the various factors contributing to the observed effects. The primary source of data will be key actors involved in the GACOs. We expect to conduct around 40 semi-structured interviews. DISCUSSION: This will be the first study in Canada to evaluate the implementation of this innovation. It will provide an exhaustive picture of the effects of GACO implementation in Quebec and to assess their potential for generalization elsewhere in Canada. At the theoretical level, this study will produce new knowledge on the factors having the greatest influence on the implementation of primary care innovations in professional environments.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".