Paramedics assessing patients with complex comorbidities in community settings: results from the CARPE study
Bibliographic record
Abstract
OBJECTIVES: The aim for this study was to provide information about how community paramedicine home visit programs best "navigate" their role delivering preventative care to frequent 9-1-1 users by describing demographic and clinical characteristics of their patients and comparing them to existing community care populations. METHODS: Our study used secondary data from standardized assessment instruments used in the delivery of home care, community support services, and community paramedicine home visit programs in Ontario. Identical assessment items from each instrument enabled comparisons of demographic, clinical, and social characteristics of community-dwelling older adults using descriptive statistics and z-tests. RESULTS: Data were analyzed for 29,938 home care clients, 13,782 community support services clients, and 136 community paramedicine patients. Differences were observed in proportions of individuals living alone between community paramedicine patients versus home care clients and community support clients (47.8%, 33.8%, and 59.9% respectively). We found higher proportions of community paramedicine patients with multiple chronic disease (87%, compared to 63% and 42%) and mental health-related conditions (43.4%, compared to 26.2% and 18.8% for depression, as an example). CONCLUSION: When using existing community care populations as a reference group, it appears that patients seen in community paramedicine home visit programs are a distinct sub-group of the community-dwelling older adult population with more complex comorbidities, possibly exacerbated by mental illness and social isolation from living alone. Community paramedicine programs may serve as a sentinel support opportunity for patients whose health conditions are not being addressed through timely access to other existing care providers. PROTOCOL REGISTRATION: ISRCTN 58273216.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".