Use of population-based data to characterize racialized and non-racialized Ontarians who self-report a past hysterectomy
Bibliographic record
Abstract
North American researchers report that women who undergo a hysterectomy for benign \nconditions are threatened by health disparities. Few studies have examined race and health in \nOntario women who underwent a past hysterectomy. The purpose of this descriptive \ncorrelational study was to describe and compare health features of racialized and non-racialized \nwomen. Using the 2011-2012 Canada’s Community Health Survey (CCHS) dataset, this study’s \nsample consisted of all Ontario residing female respondents (n = 1,730) who self-reported having \nhad a hysterectomy with no cancer history. Extracted socio-demographic and health-related \nvariables were extracted in accordance with the Gender and Equity Health Indicator Framework \n(Clark & Bierman, 2009). Chi-squares and z-scores were calculated to compare racialized and \nnon-racialized women health indicators. Many of the significant differences were found within \nthe non-medical determinants of health domain. Study implications reinforce the need for \naggregated data by race in Ontario to address health equity.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".