Wait-time reporting systems for elective surgery in Canada: a content analysis of provincial and territorial initiatives
Bibliographic record
Abstract
BACKGROUND: In Canada, a substantial barrier to the accessibility of surgical procedures is wait times. The objective of this study was to develop and describe an inventory of wait-time reporting systems for elective surgical procedures. METHODS: Between June and August 2019, we searched all Canadian provincial and territorial ministry of health websites to identify the wait-time reporting systems in place. We conducted content analysis and used a qualitative descriptive approach to compare the variables of interest across the provinces and territories. RESULTS: There were websites available for assessment in all 13 provinces and territories. Seven provinces have comprehensive, centralized wait-time reporting systems. The rest of the provinces have highly decentralized wait-time reporting, and the territories do not have wait-time reporting systems in place. There is substantial variation in the comprehensiveness, purpose, data sources and data collection methods among the wait-time reporting systems across the provinces and territories. INTERPRETATION: Wait-time reporting for elective surgery in Canada is diverse, and it varies in comprehensiveness across the provinces and territories. The present findings can help direct future investigations of Canadian reporting systems, which would provide useful information for policy-makers and those interested in reducing wait times in Canada.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".