An examination of difficulties accessing surgical care in Canada from 2005-2014: Results from the Canadian Community Health Survey
Bibliographic record
Abstract
BACKGROUND: Difficulties accessing surgical care (e.g., related to wait times, cancellations, cost, receiving a diagnosis) are understudied in Canada. Using population-based data, we studied difficulty accessing non-emergency surgical care, including (1) the incidence and annual changes in incidence, (2) types of difficulties, and (3) associated factors (e.g., sociodemographics, surgery characteristics). METHODS: Cross-sectional data from the Canadian Community Health Survey annual components were analyzed from 2005-2014. Weighted frequencies established the annual incidence of difficulty accessing surgical care, and total incidence of types of difficulties. Chi-square analyses, independent samples t-tests, and a multivariable logistic regression examined sociodemographic and surgery-related characteristics associated with difficulty accessing surgical care. RESULTS: Among individuals who required past-year non-emergency surgery between 2005-2014 (weighted n = 3,052,072), 15.6% experienced difficulty accessing surgical care. The most common difficulty was "waited too long for surgery" (58.5%). There were significant differences in the incidence of difficulty according to year (Χ2 = 83.50, p < .001) from 2005-2014. The incidence of difficulty accessing surgery varied according to sex (Χ2 = 4.02, p < .05), surgery type (Χ2 = 96.09, p < .001), party responsible for cancellation/postponement (Χ2 range: 4.36-19.01, p < .05), and waiting time (t = 10.59, p < .001). In particular, males, orthopedic surgery, and surgery cancelled by the surgeon or hospital had the highest rates of difficulty. CONCLUSION: Results provide insight into the difficulties experienced by patients accessing elective surgery, and the associated factors. These results may inform targeted healthcare interventions and resource reallocation to reduce these occurrences.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".