Physician perspectives of the community paramedicine at clinic (CP@clinic) and my care plan app (myCP app) for older adults
Bibliographic record
Abstract
BACKGROUND: Community Paramedicine (CP) is an emerging model of care addressing health problems through non-emergency services. Little evidence exists examining the integration of an app for improved patient, CP, and family physician (FP) communication. This study investigated FP perspectives on the impact of the Community Paramedicine at Clinic (CP@clinic) program on providing patient care and the feasibility and value of a novel "My Care Plan App" (myCP app). METHODS: This retrospective mixed-methods study included an online survey and phone interviews to elucidate FPs ' perspectives on the CP@clinic program and the myCP app, respectively, between January 2021 and May 2021. FPs with patients in the CP@clinic program were recruited to participate. Survey responses were summarized using descriptive statistics, and audio recordings from the interviews thematically analyzed. RESULTS: Thirty-eight FPs completed the survey and 10 FPs completed the phone interviews. 60.5% and 52.6% of FPs reported that the CP@clinic program improved their ability to further screen and diagnose patients for hypertension, respectively (in addition to their regular screening practices). The themes that emerged in the phone interviews were grouped into three topics: app benefits, drawbacks, and integration within practice. Overall, FPs described the myCP app as user-friendly and useful to improve interprofessional communication with CPs. CONCLUSIONS: CP@clinic helped family physicians to screen and monitor chronic disease. The myCP app can impact health service delivery by closing the gap between primary, community, and emergency care through an eHealth information-sharing platform.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".