EQUIP emergency: can interventions to reduce racism, discrimination and stigma in EDs improve outcomes?
Bibliographic record
Abstract
BACKGROUND: Despite a publicly funded system, health care in Canada has been shown to be deeply inequitable, particularly toward Indigenous people. Based on research identifying key dimensions of equity-oriented health care as being cultural safety, harm reduction and trauma- and violence-informed care, an intervention to promote equity at the organizational level was tested in primary health care, refined and adapted, and tested in Emergency Departments (EDs). METHODS: In partnership with clinical, community and Indigenous leaders in three diverse EDs in one Canadian province, we supported direct care staff to tailor and implement the intervention. Intervention activities varied in type and intensity at each site. Survey data were collected pre- and post-intervention from every consecutive patient over age 18 presenting to the EDs (n = 4771) with 3315 completing post-visit questions in 4 waves at two sites and 3 waves (due to pandemic constraints) at the third. Administrative data were collected for 12 months pre- and 12 months post-intervention. RESULTS: Throughout the study period, the participating EDs were dealing with a worsening epidemic of overdoses and deaths related to a toxic drug supply, and the COVID 19 pandemic curtailed both intervention activities and data collection. Despite these constraints, staff at two of the EDs mounted equity-oriented intervention strategies; the other site was experiencing continued, significant staff shortages and leadership changeover. Longitudinal analysis using multiple regression showed non-significant but encouraging trends in patient perceptions of quality of care and patient experiences of discrimination in the ED. Subgroup analysis showed that specific groups of patients experienced care in significantly different ways at each site. An interrupted time series of administrative data showed no significant change in staff sick time, but showed a significant decrease in the percentage of patients who left without care being completed at the site with the most robust intervention activities. CONCLUSIONS: The trends in patient perceptions and the significant decrease in the percentage of patients who left without care being completed suggest potential for impact. Realization of this potential will depend on readiness, commitment and resources at the organizational and systems levels. TRIAL REGISTRATION: Clinical Trials.gov #NCT03369678 (registration date November 18, 2017).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".