Through An Equity Lens: Illuminating The Relationships Among Social Inequities, Stigma And Discrimination, And Patient Experiences of Emergency Health Care
Bibliographic record
Abstract
People who experience the greatest social inequities often have poor experiences in emergency departments (EDs) so that they are deterred from seeking care, leave without care complete, receive inadequate care, and/or return repeatedly for unresolved problems. However, efforts to measure and monitor experiences of care rarely capture the experiences of people facing the greatest inequities, experiences of discrimination, or relationships among these variables. This analysis examined how patients' experiences, including self-reported ratings of care, experiences of discrimination, and repeat visits vary with social and economic circumstances. Every consecutive person presenting to three diverse EDs was invited if/when they were able to consent; 2424 provided demographic and contact information; and 1692 (70%) completed the survey. Latent class analysis (LCA) using sociodemographic variables: age, gender, financial strain, employment, housing stability, English as first language, born in Canada, and Indigenous identity, indicated a six-class solution. Classes differed significantly on having regular access to primary care, reasons for the visit, and acuity. Classes also differed on self-reported discrimination every day and during their ED visit, ratings of ED care, and number of ED visits within the past six months. ED care can be improved through attention to how intersecting forms of structural disadvantage and inequities affect patient experiences.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".