How long are Canadians waiting to access specialty care? Retrospective study from a primary care perspective.
Bibliographic record
Abstract
OBJECTIVE: To calculate patient wait times for specialist care using data from primary care clinics across Canada. DESIGN: Retrospective chart audit. SETTING: Primary care clinics. PARTICIPANTS: A total of 22 primary care clinics across 7 provinces and 1 territory. MAIN OUTCOME MEASURES: Wait time 1, defined as the period between a patient's referral by a family physician to a specialist and the visit with said specialist. RESULTS: Overall, 2060 referrals initiated between January 2014 and December 2016 were included in the analysis. The median national wait time 1 was 78 days (interquartile range [IQR] of 34 to 175 days). The shortest waits were observed in Saskatchewan (51 days; IQR = 23 to 101 days) and British Columbia (59 days; IQR = 29 to 131 days), whereas the longest were in New Brunswick (105 days; IQR = 43 to 242 days) and Quebec (104 days; IQR = 36 to 239 days). Median wait time 1 varied substantially among different specialty groups, with the longest wait time for plastic surgery (159 days; IQR = 59 to 365 days) and the shortest for infectious diseases (14 days; IQR = 6 to 271 days). CONCLUSION: This is the first national examination of wait time 1 from the primary care perspective. It provides a picture of patient access to specialists across provinces and specialty groups. This research provides decision makers with important context for developing programs and policies aimed at addressing the largely ignored stage of the wait time continuum from the time of referral to eventual appointment time with the specialist.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 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".