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 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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 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".