Bibliographic record
Abstract
Inappropriate ambulance use can be defined broadly as the use of emergency medical services (EMS) transport for non-urgent medical conditions, or when the patient does not use alternate transportation available. It drains health system resources, contributes to low morale among paramedics, and can delay care for patients who may be appropriately treated in alternative settings. An increasing number of studies indicate that inappropriate EMS use occurs, but few studies investigate how perspectives of inappropriate use are constructed. This study explores the construct of appropriateness in the context of ambulance use, and examines the implications of varying perspectives on ambulance billing policies. We present a grounded theory on the construct of appropriate ambulance use from interviews with paramedics in Ontario, national media reports and online reader commentary. Findings show that the role of paramedics varies across regions, and includes various types of care (e.g., emergency response, primary care and preventative care), and transportation (e.g., to the emergency department or urgent care clinics). In turn, constructs of appropriateness vary. In ambiguous cases, paramedics use their perception of the patients’ ability or attempts to cope with the medical situation to evaluate the appropriateness of ambulance use. Unexpectedly, the most frustrating cases of inappropriate ambulance use tend to be initiated by organizations, such as long-term care facilities, rather than members of the general public. These findings raise questions about the potential for ambulance user fees conditional on ‘appropriateness’ to improve either the effectiveness or the efficiency of ambulance services.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".