Bibliographic record
Abstract
PROBLEM BEING ADDRESSED: Patients in a family practice had to wait 6 weeks for an appointment. OBJECTIVE OF PROGRAM: To improve patient care by facilitating access to timely appointments. PROGRAM DESCRIPTION: An FP from Halifax, NS, implemented advanced access in her practice. Advanced access is a same-day booking system, which has been shown to reduce or eliminate patient backlogs without the addition of resources. Theoretically, it can be put into effect in any practice with a stable backlog (which indicates that supply and demand are well matched). The first step to implement the advanced access system was to clear the existing appointment backlog. During a 6-week "boot camp" period, all prebooked patients and patients who called requesting same-day appointments were seen (between 50 and 60 patients per day). Same-day appointment rules apply to almost all patients. Staff begin accepting calls at 8:00 AM, and patients request the most convenient time available. CONCLUSION: Baseline and postimplementation data are not available, as this was not a formal research study. Nevertheless, this FP from Halifax who implemented advanced access experienced the following in her practice: elimination of patient backlog, fewer no-shows, patients' happiness with the system, increase in physician and staff morale, and stability in physician income. Formal feasibility studies and research evaluating patient outcomes, cost effectiveness, and physician and patient satisfaction in a variety of practice settings would help Canadian FPs decide if same-day booking could be successfully implemented in their practices.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".