Equity in Canadian health care: does socioeconomic status affect waiting times for elective surgery?
Bibliographic record
Abstract
BACKGROUND: Waiting times for surgical and other procedures are an important measure of how well the health care system responds to patient needs. In a universal health care system such as Canada's, it is important to determine if waiting times vary by socioeconomic status (SES). We compared waiting times for elective surgery of patients living in low and high socioeconomic areas. METHODS: We reviewed the medical charts of all patients who underwent elective surgery at a Canadian academic health centre between 1992 and 1999. Using patient postal codes we assigned SES on the basis of 5 characteristics in the 1996 census data. We compared waiting times for surgery for people from regions in the lowest third (low SES group) with that for patients from regions in the upper third (high SES group). RESULTS: On average, patients in the high SES group waited 31.1 days and those in the low SES group waited 29.3 days. When differences in waiting times for 22 common procedures were examined between the groups, only the difference for prostatectomy was statistically significant: patients in the high SES group waited 4.4 fewer days than those in the low SES group. INTERPRETATION: We found little evidence that residing in a region in which SES was in the lowest third was associated with longer waiting times for elective surgery.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".