How long are Canadians waiting to access specialty care? Retrospective study from a primary care perspective.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it