Facilitators and barriers to atrial fibrillation screening in primary care: a qualitative descriptive study of GPs in primary care in the Republic of Ireland
Bibliographic record
Abstract
BACKGROUND: Atrial fibrillation (AF), the most common cardiac arrhythmia, is a major risk factor for stroke. AF is often asymptomatic, making it difficult to diagnose. Globally, stroke is a leading cause of morbidity and mortality. Opportunistic AF screening has been recommended in clinical practice within the Republic of Ireland (RoI) and internationally, though the optimal mode and location remains under investigation. Currently, there is no formal AF screening programme. Primary care has been proposed as a suitable setting. AIM: To identify the facilitators and barriers to AF screening in primary care from the perspective of GPs. DESIGN & SETTING: A qualitative descriptive study design was adopted. Fifty-four GPs were invited from 25 practices in the RoI to participate in individual interviews at their practices. Participants were from both rural and urban locations. METHOD: A topic guide was developed to guide the interview content towards identification of facilitators and barriers to AF screening. The interviews were conducted in person, audio-recorded, transcribed verbatim, and analysed using framework analysis. RESULTS: Eight GPs from five practices participated in an interview. Three GPs, two male and one female, were recruited from two rural practices and five GPs, two male and three female, were recruited from three urban practices. All eight GPs expressed a willingness to engage in AF screening. Time pressures and the need for additional staff to support were identified as barriers. Programme structure and patient awareness campaigns and education were identified as facilitators. CONCLUSION: The findings will help to anticipate barriers to AF screening and aid the development of clinical pathways for people with or at risk of AF. The results have been integrated into a pilot primary care-based screening programme for AF.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".