10 years later—a portrait of advanced access implementation by family physicians and nurse practitioners in Quebec, Canada
Bibliographic record
Abstract
<h3>Context:</h3> The advanced access model is the most recommended innovation around the world to improve timely access and support patients’ needs for relational and informational continuity. Originally developed in the United States in 2002, the advanced access model has been widely promoted by the College of Family Physicians of Canada since 2012. <h3>Objective:</h3> To present a portrait of the implementation of the advanced access model 10 years after its large-scale introduction across the province of Quebec. <h3>Study Design and Analysis:</h3> We conducted a cross-sectional e-survey based on a self-reported online reflective tool (Outil Réflexifsur l’Accès Adapté; ORAA) between January 2022 and February 2023. Descriptive statistics were generated for all items. Setting or Dataset: Medical clinics across 14 regions of the province of Quebec participated. Population Studied: 999 family physicians, 107 nurse practitioners and 411 administrative officers from 127 clinics responded to the ORAA. <h3>Instrument:</h3> The ORAA is a 39-item e-survey developed to present a portrait of the level of implementation of recommended advanced access strategies. <h3>Outcome Measures:</h3> The actual degree of implementation of key pillars of the advanced access model. <h3>Results:</h3> Results showed variations across different key strategies. Opening the schedule for appointments over a period of 2 to 4 weeks was largely adopted by a total of 82% of respondents. However, another key strategy, planning to reserve consultation time for urgent or semi-urgent conditions, was implemented by less than 50% of respondents. Similarly, only 33% of the administrative officers surveyed reported using a referral algorithm to book appointments with the appropriate provider in a timely manner. Planning for the supply and offer for the upcoming year based on the number and characteristics of the patient base was poorly implemented, only 17% planned for supply and 13% for offer. Finally, strategies to react to unforeseen offer-demand imbalances were insufficiently put in place; less than half of respondents adopted this strategy. <h3>Conclusions:</h3> This study demonstrates that few advanced access strategies have been successfully implemented. More strategies are needed to assess the supply-demand balance and to react to imbalances when they occur. We demonstrate that strategies based on individual practice change are more often implemented than those requiring changes at the clinic level.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".