Home Visit‐Based Community Paramedicine and Its Potential Role in Improving Patient‐Centered Primary Care: A Grounded Theory Study and Framework
Bibliographic record
Abstract
OBJECTIVE: Community paramedicine (CP) is a model of community-based health care being used around the world. Our objective was to study the patient perspective and valuation of this type of program to understand its potential value for primary care innovation in the future. STUDY SETTING: The EPIC community paramedicine program is a partnership between primary care physicians and specially trained community paramedics, designed to provide in-home support for complex chronic disease patients in Ontario, Canada. STUDY DESIGN: As part of an ongoing clinical trial we designed an embedded qualitative evaluation using constructionist grounded theory methodology. DATA COLLECTION METHODS: Data collection included in-depth interviews with 30 patients and/or family members and 60 hours of observation. PRINCIPAL FINDINGS: The health care needs of this complex population are largely attributes that impact a patient's quality of life-including recognition of their vulnerability, providing a safety-net in times of exacerbation and health education and accountability. This seems to be facilitated by a relationship with a dedicated provider that increases continuity of care. CONCLUSIONS: Home-based community paramedicine programs like EPIC appear to be able to create a patient-centered, safe, responsive therapeutic relationship that is often not possible within the standard primary health care 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 | 0.016 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".