Comparing the implementation of advanced access strategies among primary health care providers
Bibliographic record
Abstract
The advanced access (AA) model is among the most recommended innovations for improving timely access in primary health care (PHC). Originally developed for physicians, it is now relevant to evaluate the model’s implementation in more interprofessional practices. We compared AA implementation among family physicians, nurse practitioners, and nurses. A cross-sectional online open survey was completed by 514 PHC providers working in 35 university-affiliated clinics. Family physicians delegated tasks to other professionals in the team more often than nurse practitioners (p = .001) and nurses (p < .001). They also left a smaller proportion of their schedules open for urgent patient needs than did nurse practitioners (p = .015) and nurses (p < .001). Nurses created more alternatives to in-person visits than family physicians (p < .001) and coordinated health and social services more than family physicians (p = .003). During periods of absence, physicians referred patients to walk-in services for urgent needs significantly more often than nurses (p = .003), whereas nurses planned replacements between colleagues more often than physicians (p <.001). The variations among provider categories indicate that a one-size-fits-all implementation of AA principles is not recommended.
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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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".