Community paramedicine: cost–benefit analysis and safety evaluation in paramedical emergency services in rural areas – a scoping review
Bibliographic record
Abstract
OBJECTIVE: To examine the current knowledge and possibly identify gaps in the knowledge base for cost-benefit analysis and safety concerning community paramedicine in rural areas. DESIGN: Scoping review. DATA SOURCES: MEDLINE via PubMed, CINAHL, Cochrane and Embase up to December 2020. STUDY SELECTION: All English studies involving community paramedicine in rural areas, which include cost-benefit analysis or safety evaluation. DATA EXTRACTION: This scoping review follows the methodology developed by Arksey and O'Malley and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. We systematically searched for all types of studies in the databases and the reference lists of key studies to identify studies for inclusion. The selection process was in two steps. First, two reviewers independently screened 2309 identified articles for title and abstracts and second performed a full-text review of 24 eligible studies for inclusion. RESULTS: Three articles met the inclusion criteria concerning cost-benefit analysis, two from Canada and one from USA. No articles met the inclusion criteria for safety evaluation. CONCLUSION: There are knowledge gaps concerning safety evaluation of community paramedicine in rural areas. Three articles were included in this scoping review concerning cost-benefit analysis, two of them showing positive cost-effectiveness with community paramedicine in rural areas.
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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 | 0.017 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".