The impact of community paramedicine interventions on Transfers from Long Term Care to Emergency Departments: A systematic review
Bibliographic record
Abstract
OBJECTIVES: Long term care (LTC) facilities are essential in the provision of daily care needs for older adults experiencing frailty. In times of acute medical distress, LTC residents may require transfer to emergency departments (ED). However, many transfers from the LTC to ED may not be required and residents could instead be treated within LTC utilizing community paramedics (CP). We conducted a systematic review to assess the impact of community paramedicine on the frequency of resident transfer from LTC to ED. METHODS: We searched five electronic databases (Medline, CINAHL, PubMed, Embase, and Cochrane). The search was limited to primary peer-reviewed publications and excluded conference proceedings, review articles, and non-peer review articles. We restricted the search to papers published in English and articles published within the last 30 years. RESULTS: A total of 19,308 titles and abstracts were screened with a total of 181 full text reviews. One study that comprised of 4 articles was included in the review that evaluated the impact of community paramedic interventions in LTC. The study noted a reduction in transfers to the ED attributed to the presence of CP, reducing transports to ED by nearly 30%. CONCLUSIONS: There is a scarcity of research examining the role of CP in LTC. While the current research points towards CP interventions in LTC reducing the number of transfers to ED, further research needs to be conducted on the effectiveness of community paramedicine interventions in improving health outcomes for residence and improving cost efficiency within the health system.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".