Equity‐relevant sociodemographic variable collection in emergency medicine: A systematic review, qualitative evidence synthesis, and recommendations for practice
Bibliographic record
Abstract
OBJECTIVES: The objective was to conduct a systematic review and qualitative evidence synthesis (QES) to identify best practices, benefits, harms, facilitators, and barriers to the routine collection of sociodemographic variables in emergency departments (EDs). METHODS: This work is a systematic review and QES. We conducted a comprehensive search of Medline (Ovid), CINAHL (Ebsco), Cochrane Central (OVID), EMBASE (Ovid), and the multidisciplinary Web of Science Core database using peer-reviewed search strategies, complemented by a gray literature search. We included citations containing perspectives on routine sociodemographic variable collection in EDs and recommendations on definitions or processes of collection or benefits, harms, facilitators, or barriers related to the routine collection of sociodemographic variables in EDs. We conducted this systematic review and QES adhering to the Joanna Briggs Institute guidelines. Two reviewers independently selected included studies and extracted data. We conducted a best-fit framework synthesis and paired inductive thematic analysis of the included studies. We generated recommendations based on the QES. RESULTS: We included 21 unique reports that enrolled 10,454 patients or respondents in our systematic review and QES. Publication dates of included studies ranged from 2011 to 2021. Included citations were published in Australia, Canada, and the United States. We synthesized 11 benefits, 14 potential harms, 15 barriers, and 19 facilitators and identified 14 best practice recommendations from included citations. CONCLUSIONS: Health systems should routinely collect sociodemographic variables in EDs guided by recommendations that minimize harms and maximize benefits and consider relevant barriers and facilitators. Our recommendations can serve as a guide for the equity-focused reformation of emergency medicine health information systems.
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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 | 0.019 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".