Compatibility of Quebec Residency Program Characteristics with the Advanced Access Model: A Cross-Sectional Study
Bibliographic record
Abstract
<h3>Context</h3> The advanced access (AA) model is based on several core pillars, including balancing appointment supply and demand, regularly adjusting supply, optimizing appointment systems and collaboration practices. Exposure of family medicine resident to AA within university family medicine groups (U-FMGs) is a promising strategy to widen its dissemination. It is also essential to reflect on aspects of the model that could be adopted as well as those requiring adaptation to improve the residency experience. <h3>Objective</h3> Determine compatibility of U-FMG residency programs with AA pillars. Study Design and Analysis Cross-sectional survey of local residency program characteristics with respect to AA pillars. Program compatibility with 4 AA pillars was categorized inductively by a research team that included clinicians, AA experts, a residency program expert and 4 family medicine residents. <h3>Setting</h3> 46 U-FMGs in Quebec, Canada <h3>Population studied</h3> The chief resident and academic director of each U-FMG <h3>Instrument</h3> A de novo online survey questionnaire piloted with 3 former chief residents. The survey included 32 questions about family group practices, residency rotation programs, opportunities to collaborate with other professionals and training received on AA. The questionnaire and 2 reminders were sent by email to all respondents. <h3>Outcome measures</h3> Characteristics of residency programs were grouped by AA pillar. A three-level score (compatible, moderately compatible, or not very compatible with the principles of AA) was assigned to each group of characteristics of local U-FMG residency programs. <h3>Results</h3> 38 of 46 U-FMGs participated (82.6%). No U-FMG obtained a score of compatible for all 4 pillars considered. Balancing appointment supply and demand appeared to be adequate for >70% of U-FMGs. However, 60% of U-FMG appointment systems were not very compatible with the AA model, mostly because the proportion of the schedule reserved for urgent appointments was insufficient. Opportunities for collaboration were compatible with AA principles in 32% of programs. Almost 66% of programs offered training on AA. <h3>Conclusions</h3> Our study highlights the heterogeneity among residency programs with respect to their compatibility with AA. Our theoretical approach limits the objective assessment of the impact on timely access for patients. A second part of the project correlating AA indicators with residency program characteristics is ongoing.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".