Community Health Evaluations Completed Using Paramedic Service (Checups): Design and Implementation of A New Community-Based Health Program
Bibliographic record
Abstract
The Government of Ontario established a one-time funding program intended to create a Community Paramedicine best practice in support of its Action Plan for Health Care. The County of Renfrew Community Resilience Program responded with the creation of the CHECUPS program. The study was conducted in the County of Renfrew, Ontario, Canada where a Community Resilience Program expanded to include the CHECUPS Program. The evaluation of the CHECUPS program has addressed impacts to three domains: 1) patient overall health and satisfaction; 2) primary care integration; and 3) paramedic resource utilization. The results included a total of 222 patients that demonstrated a 24% reduction in 911 activation; 20% reduction in repeat ED visits; 55% decrease in patients that were admitted post ED visits; and all patients indicated that they were either “satisfied” or “very satisfied” with the care provide by community paramedics. The CHECUPS Community Paramedic Program is in an excellent position to support the Province of Ontario Action Plan for Health Care by responding to the increasing emergency response demands, chronic pressures within the health care system, and need to provide a more sustainable, integrated, patient-centred system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".