Experience with sociodemographic data collection in the Canadian paediatric surgical context: A quality improvement initiative
Bibliographic record
Abstract
Groups that experience social inequities have poorer health outcomes; however, Canadian healthcare institutions do not routinely collect data to identify those with health inequities. This quality improvement initiative assessed response rates for different methods of sociodemographic data collection using a questionnaire developed to support the ethical prioritization of paediatric non-urgent surgery. Of the 329 families contacted, 85.4% (281/329) completed the questionnaire and of those, 79.7% (224/281) provided sociodemographic data. Surgeon asking in the clinic had the highest response rate (100.0%, 5/5), followed by phone calls from surgical booking administrators (81.6%, 93/114), and a research assistant asking in the clinic (81.0%, 34/42). Sociodemographic data collection is feasible in a Canadian paediatric hospital setting and response rates were higher when completed in person and by staff supporting the care of the patient. The next steps will be to incorporate patient social determinants of health data into decision-making for surgical prioritization.
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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 | 0.223 | 0.221 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.001 | 0.005 |
| 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".