EQUIP Emergency: study protocol for an organizational intervention to promote equity in health care
Bibliographic record
Abstract
BACKGROUND: Social inequities are widening globally, contributing to growing health and health care inequities. Health inequities are unjust differences in health and well-being between and within groups of people caused by socially structured, and thus avoidable, marginalizing conditions such as poverty and systemic racism. In Canada, such conditions disproportionately affect Indigenous persons, racialized newcomers, those with mental health and substance use issues, and those experiencing interpersonal violence. Despite calls to enhance equity in health care to contribute to improving population health, few studies examine how to achieve equity at the point of care, and the impacts of doing so. Many people facing marginalizing conditions experience inadequate and inequitable treatment in emergency departments (EDs), which makes people less likely to access care, paradoxically resulting in reliance on EDs through delays to care and repeat visits, interfering with effective care delivery and increasing human and financial costs. EDs are key settings with potential for mitigating the impacts of structural conditions and barriers to care linked to health inequities. METHODS: EQUIP is an organizational intervention to promote equity. Building on promising research in primary health care, we are adapting EQUIP to emergency departments, and testing its impact at three geographically and demographically diverse EDs in one Canadian province. A mixed methods multisite design will examine changes in key outcomes including: a) a longitudinal analysis of change over time based on structured assessments of patients and staff, b) an interrupted time series design of administrative data (i.e., staff sick leave, patients who leave without care being completed), c) a process evaluation to assess how the intervention was implemented and the contextual features of the environment and process that are influential for successful implementation, and d) a cost-benefit analysis. DISCUSSION: This project will generate both process- and outcome-based evidence to improve the provision of equity-oriented health care in emergency departments, particularly targeting groups known to be at greatest risk for experiencing the negative impacts of health and health care inequities. The main deliverable is a health equity-enhancing framework, including implementable, measurable interventions, tested, refined and relevant to diverse EDs. TRIAL REGISTRATION: Clinical Trials.gov # NCT03369678 (registration date November 18, 2017).
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".