What Are the Factors Influencing Implementation of Advanced Access in Family Medicine Units? A Cross-Case Comparison of Four Early Adopters in Quebec
Bibliographic record
Abstract
INTRODUCTION: Advanced access is an organizational model that has shown promise in improving timely access to primary care. In Quebec, it has recently been introduced in several family medicine units (FMUs) with a teaching mission. The objectives of this paper are to analyze the principles of advanced access implemented in FMUs and to identify which factors influenced their implementation. METHODS: A multiple case study of four purposefully selected FMUs was conducted. Data included document analysis and 40 semistructured interviews with health professionals and staff. Cross-case comparison and thematic analysis were performed. RESULTS: Three out of four FMUs implemented the key principles of advanced access at various levels. One scheduling pattern was observed: 90% of open appointment slots over three- to four-week periods and 10% of prebooked appointments. Structural and organizational factors facilitated the implementation: training of staff to support change, collective leadership, and openness to change. Conversely, family physicians practicing in multiple clinical settings, lack of team resources, turnover of clerical staff, rotation of medical residents, and management capacity were reported as major barriers to implementing the model. CONCLUSION: Our results call for multilevel implementation strategies to improve the design of the advanced access model in academic teaching settings.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".