Surgery Delays and Cancellations in Canada: A Preliminary Examination of Prevalence Trends Over the Past Decade
Bibliographic record
Abstract
Introduction: The current Canadian health care system is associated with long wait times, where short notice cancellations occur and can be associated with significant disappointment/frustration for the patient, reduction in theatre efficiency/time, increased hospital expenditures, and decreased staff morale. There is no study to date that has identified the prevalence of difficulties acquiring elective surgical procedures in Canada. Methods: The data were drawn from the cross-sectional nationally representative Canadian Community Health Survey (CCHS). We calculated weighted frequencies and supplied 95% confidence intervals to assess prevalence rates of patients receiving past-year non-emergency surgery and prevalence of respondents who had difficulties acquiring surgery for each year. CCHS data files between 2005-2014 were then collapsed to a single aggregate data file to assess the prevalence of type of difficulty experienced. Weighted cross-tabulations, chi-square analyses, and t-tests were used to assess difficulties acquiring surgery across sociodemographic variables, surgery characteristics variables, and waiting times. Results: Results of the chi-square analyses suggest a significance in the prevalence of non-emergency surgery by year (X2 = 67.5, p <.001) and of difficulties acquiring surgery according to year (X2 = 83.5, p <.001). The most prevalent type of difficulty respondents endorsed for acquiring surgery was that respondents waited too long for surgery (58.5%). The results of weighted cross-tabulations and chi-square analyses indicated that the prevalence of difficulties acquiring surgery significantly differed according to sex (X2 = 4.02, p<.05). There was also significant associations between difficulty acquiring surgery with surgery and waiting time variables.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.016 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".