One Size Does not Fit All – A Realist Review of Screening Asymptomatic Atrial Fibrillation in Indigenous Communities in Australia, Canada, New Zealand, and the USA
Bibliographic record
Abstract
The true prevalence of atrial fibrillation (AF) is underestimated because asymptomatic AF is underdetected. Adverse consequences of AF such as stroke may occur before AF is diagnosed. Current guidelines recommend opportunistic screening of AF in the general population for patients 65 years and older; however, this might not be suitable for Indigenous people. Screening for AF meets the World Health Organization criteria for successful routine screening, yet little is known about successful implementation of AF screening in Indigenous communities. This study uses a realist review methodology and framework to identify what works, how, for whom, and under what circumstances for AF screening in Indigenous communities. Eight databases and gray literature were searched for studies targeted at AF screening in Indigenous communities. Realist analysis was used to identify context-mechanism-outcome configurations across 11 included records. Some mechanisms that improve AF screening in Indigenous communities were identified. Salient enablers of AF screening in Indigenous communities include opportunistic nonclinical settings, portable electrocardiogram devices, and increasing training in Indigenous health-care workers. Tailoring follow-up protocols that are geographically and culturally appropriate to the settings is important. Prominent barriers included lack of cultural safety, fear of abnormal results, and time-poor environments. A middle-range theory is proposed in combination with the Indigenous health promotion tool model. Indigenous populations require earlier screening and culturally safe approaches for AF detection and pathways to treatment. A novel AF screening strategy is required. This realist review provides lessons learned for the successful implementation of AF screening and treatment programs for Indigenous communities.
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 | 0.028 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".