Barriers to the use of emergency medical services for ST‐elevation myocardial infarction: Determining why many patients opt for self‐transport
Bibliographic record
Abstract
RATIONALE, AIMS, AND OBJECTIVES: Access to timely ST-elevation myocardial infarction (STEMI) care is facilitated by paramedics and emergency medical services (EMS). However, a large proportion of STEMI patients do not access care through EMS. This study sought to identify patient-reported factors for their decision to use (or not use) EMS. METHODS: Semi-structured interviews were conducted with a sample of STEMI patients admitted to a large tertiary care centre between November 2011 and January 2012. Participants were grouped according to mode of transportation to hospital at time of index event (EMS vs self-transport). Participant responses were classified using a published framework (modified for a STEMI population) as barriers or facilitators to EMS use, and compared between groups. RESULTS: Data were collected on 61 patients (32 EMS, 29 self-transport). Mean age was 60.3 (SD 11.5), and 23% were female. EMS users were more likely to have a Killip Class >1 (25% vs 4%; P = 0.03). Self-transport patients were more likely to perceive EMS as slower (48% vs 0%) and express concerns over resources misuse (34% vs 3%; P = 0.002), when compared to EMS patients. Patients who accessed EMS were more likely to acknowledge the benefits of EMS (44% vs 7%; P = 0.001) and were more likely to have been encouraged by a family member to call EMS (34% vs 4%; P = 0.003). CONCLUSIONS: STEMI patient perceptions are a key factor in determining EMS use. Health care stakeholders should target the identified barriers to improve utilization of EMS, and develop strategies to optimize care for patients who do not access EMS.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".