The Association of the Frequency of Community Paramedicine Sessions and 9-1-1 Calls in Ontario Subsidized Housing: A Multilevel Analysis
Bibliographic record
Abstract
Older adults, especially those who are of low socioeconomic status, experience higher rates of mortality and chronic disease. As a result, older adults are frequent users of emergency medical service (EMS), comprising approximately 38-48% of all EMS calls. In response to higher EMS demands, community paramedicine has recently emerged as a non-traditional model whereby paramedics provide care in a community- based setting. CP@clinic is a community paramedicine programme that focuses on disease prevention and health promotion with the goal of reducing EMS demand. Given the knowledge that older adults who live in subsidized housing have poorer health outcomes, CP@clinic has been implemented in several subsidized housing building across Ontario. A program evaluation of CP@clinic is currently underway to make recommendations to paramedic partner stakeholders regarding program delivery. As part of this evaluation, I sought to understand the association of the number of CP@clinic sessions held per month and EMS calls per apartment unit. De-identified EMS call data were collated from 9 paramedic services across Ontario from February 2015 to December 2019. I conducted a three-level multilevel regression analysis, with EMS calls per apartment unit as the outcome. The primary analysis found that a one-session increase in the number of sessions held per month was associated with an average 2.4% higher incident rate of EMS calls, adjusted for building size. A secondary analysis, with the number of sessions per month as a categorical variable, revealed that two CP@clinic sessions per month had the smallest association with EMS calls, adjusted for building size. Based on these results, it is recommended that paramedic services offer two or more CP@clinic sessions per month. Future research should investigate the factors that impact each services’ ability to offer the CP@clinic programme.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".