Determinants of Waiting Time for a Routine Family Physician Consultation in Southwestern Ontario
Bibliographic record
Abstract
Waiting times are a reality in Canada' s publicly financed single-payer healthcare system.While there are ample data about waiting times for specialized investigations and procedures, few data exist about waiting times to see family physicians, and determinants of this wait.We analyzed data from a survey of 731 family physicians in southwestern Ontario to understand physician-and practice-level determinants of waiting time.Physician gender, usual number of patients seen per week, involvement in teaching and population served were the key determinants of physician-reported waiting time. RésuméLes temps d' attente sont une réalité du système de soins de santé canadien -un système à payeur unique financé par l'État.Bien qu'il existe amplement de données sur les temps d' attente pour les enquêtes et procédures spécialisées, il en existe peu sur les temps d' attente pour consulter les médecins de famille et sur les facteurs déterminants de ces temps d' attente.Nous avons analysé des données provenant d'une enquête menée auprès de 731 médecins de famille du sud-ouest de l'Ontario afin de comprendre les facteurs déterminants liés aux médecins et à leur pratique et qui influent sur les temps d' attente.Notre recherche démontre que le sexe du médecin, le nombre habituel
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".