A qualitative study to identify factors that influence patients’ decisions to call Emergency Medical Services for syncope
Bibliographic record
Abstract
Abstract BACKGROUND Emergency medical services (EMS) assessment and presentation to the emergency department (ED) for syncope (fainting) often results in hospital admission and costly diagnostic testing that rarely benefits patients. Protocols that support paramedics to assess, treat and refer low-risk syncope may allow for ED transport of only high-risk patients. The development and successful uptake of such protocols is limited by a dearth of information about factors patients consider when deciding to seek EMS care following syncope. OBJECTIVE We aimed to explore decision-making processes of individuals with syncope when deciding whether (or not) to call EMS after fainting as a starting point in the development of prehospital risk-stratification protocols. METHODS Twenty-five adults (aged 18–65 years) with a history of ≥ 1 syncopal episode were recruited across four Canadian provinces. Individual semi-structured interviews were conducted, recorded, and transcribed. Straussian grounded theory methods were used to identify common themes and a core (overarching) category. RESULTS Four common themes were identified: (a) previous experiences with the healthcare system (e.g., feeling dismissed), (b) individual patient factors (e.g., age, comorbidities), (c) attitudes and beliefs (e.g., concerns about burdening the health care system, believing syncope is “not serious enough” to seek care), and (d) contextual factors (i.e., influence of important others, symptom severity). Perceived judgement of the patient by EMS was identified as the overarching core category that influenced patients’ decision-making to seek care. CONCLUSION The decision to seek EMS care after syncope is a complex process involving patient consideration of past experiences, pre-existing attitudes and beliefs, and individual- and contextual factors. Patients’ previous experiences of judgement and dismissal by EMS for fainting may interfere with patient receptiveness to traditional EMS protocols for syncope. These barriers could be targets for training and education for paramedics and help to inform the development of prehospital protocols to improve care and satisfaction among patients with syncope.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".