Effectiveness of the community paramedicine at home (CP@home) program for frequent users of emergency medical services in Ontario: a randomized controlled trial
Bibliographic record
Abstract
OBJECTIVE: To evaluate the impact of the Community Paramedicine at Home (CP@home) program, a community paramedicine home-visit intervention, on reducing emergency medical services (EMS) calls among frequent users. DESIGN: A 6-month, open-label, pragmatic, randomized controlled trial with parallel intervention and control arms. An online automated platform (randomizer.org) was used to randomly allocate participants using a 1:1 allocation sequence. SETTING: In homes of frequent EMS users in four paramedic services and regions across Ontario, Canada. PARTICIPANTS: Eligible participants were frequent callers (≥ 3 EMS calls within six months and ≥ 1 EMS call within the previous month), or had ≥ 1 lift assist call within the previous month, or were referred by paramedics. INTERVENTION: Community paramedics conducted risk assessments, provided health education, referred appropriate resources, and reported to family physicians for up to three home visits. The control arm received usual care. PRIMARY OUTCOME MEASURE: EMS calls in 6 months during intervention. RESULTS: Two thousand two hundred eighty four eligible participants were randomly allocated to the intervention and control groups, with 265 participants lost to follow-up due to inability to retrieve participant records from EMS databases. There were 1025 intervention participants (52.7% female, mean age 69.65 years [standard deviation (SD) = 19.98]) and 994 control participants (52.0% female, mean age 69.78 years [SD = 19.09]). In the post-intervention intention-to-treat analysis (zero-inflated negative binomial regression), the EMS call rate was not significantly lower in the intervention group compared to the control group (incidence rate ratio [IRR] = 0.88, 95% confidence interval [CI]: 0.76, 1.01). In the subgroup analysis, the intervention had a significant effect in the lift assist caller subgroup (IRR = 0.73, 95% CI: 0.58, 0.92), but no significant effect among the frequent caller subgroup (IRR = 0.97, 95% CI: 0.82, 1.14). The sensitivity analyses found a similar association for the lift assist caller subgroup. There was a significant subgroup effect (p-value for interaction < 0.01). CONCLUSIONS: CP@home had a significant impact on reducing EMS calls for those with a lift assist call but not for the overall sample. This program filled a healthcare gap by shifting primary care delivery, which could reduce the disproportionate number of EMS calls, thus reducing healthcare costs. TRIAL REGISTRATION: Registered with ClinicalTrials.gov NCT02835989 on July 14, 2016.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".