Reasons for Ethnic Disparities in the Prehospital Care Pathway Following an Out-of-Hospital Cardiac Event: Protocol of a Systematic Review
Bibliographic record
Abstract
BACKGROUND: Substantial inequities in cardiovascular disease occur between and within countries, driving much of the current burden of global health inequities. Despite well-established treatment protocols and clinical interventions, the extent to which the prehospital care pathway for people who have experienced an out-of-hospital cardiac event (OHCE) varies by ethnicity and race is inconsistently documented. Timely access to care in this context is important for good outcomes. Therefore, identifying any barriers and enablers that influence timely prehospital care can inform equity-focused interventions. OBJECTIVE: This systematic review aims to answer the question: Among adults who experience an OHCE, to what extent and why might the care pathways in the community and outcomes differ for minoritized ethnic populations compared to nonminoritized populations? In addition, we will investigate the barriers and enablers that could influence variations in the access to care for minoritized ethnic populations. METHODS: This review will use Kaupapa Māori theory to underpin the process and analysis, thus prioritizing Indigenous knowledge and experiences. A comprehensive search of the CINAHL, Embase, MEDLINE (OVID), PubMed, Scopus, Google Scholar, and Cochrane Library databases will be done using Medical Subject Headings terms themed to the 3 domains of context, health condition, and setting. All identified articles will be managed using an Endnote library. To be included in the research, papers must be published in English; have adult study populations; have an acute, nontraumatic cardiac condition as the primary health condition of interest; and be in the prehospital setting. Studies must also include comparisons by ethnicity or race to be eligible. Those studies considered suitable for inclusion will be critically appraised by multiple authors using the Mixed Methods Appraisal Tool and CONSIDER (Consolidated Criteria for Strengthening the Reporting of Health Research Involving Indigenous Peoples) framework. Risk of bias will be assessed using the Graphic Appraisal Tool for Epidemiology. Disagreements on inclusion or exclusion will be settled by a discussion with all reviewers. Data extraction will be done independently by 2 authors and collated in a Microsoft Excel spreadsheet. The outcomes of interest will include (1) symptom recognition, (2) patient decision-making, (3) health care professional decision-making, (4) the provision of cardiopulmonary resuscitation, (5) access to automated external defibrillator, and (6) witnessed status. Data will be extracted and categorized under key domains. A narrative review of these domains will be conducted using Indigenous data sovereignty approaches as a guide. Findings will be reported according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines. RESULTS: Our research is in progress. We anticipate the systematic review will be completed and submitted for publication in October 2023. CONCLUSIONS: The review findings will inform researchers and health care professionals on the experience of minoritized populations when accessing the OHCE care pathway. TRIAL REGISTRATION: PROSPERO CRD42022279082; https://tinyurl.com/bdf6s4h2. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/40557.
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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 | 0.092 | 0.092 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.017 | 0.021 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.043 | 0.005 |
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".