Perceptions of the first family physicians to adopt advanced access in the province of Quebec, Canada
Bibliographic record
Abstract
In Quebec, several primary care physicians have made the transition to the advanced access model to address the crisis of limited access to primary care. The objectives are to describe the implementation of the advanced access model, as perceived by the first family physicians; to analyze the factors influencing the implementation of its principles; and to document the physicians' perceptions of its effects on their practice, colleagues and patients. Qualitative methods were used to explore, through semi-structured interviews, the experiences of 21 family physicians who had made the transition to advanced access. Of the 21 physicians, 16 succeeded in adopting all five advanced access principles to varying degrees. Core implementation issues revolved around the dynamics of collaboration between physicians, nurses and other colleagues. Secretaries' functions, in particular, had to be expanded. Facilitating factors were mainly related to the physicians' leadership and the professional resources available in the organizations. Impediments related to resource availability and team functioning were also encountered. This is the first exploratory study to examine the factors influencing the adoption of the advanced access model conducted with early-adopter family physicians. The lessons drawn will inform discussions on scaling up to other settings experiencing the same problems. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".