Adapting the Community Paramedicine at Clinic (CP@clinic) program to a remote northern First Nation community: a qualitative study of community members’ and local health care providers’ views
Bibliographic record
Abstract
The views of community Elders and health care providers in a rural remote First Nation community in Ontario, Canada on their health care landscape and adapting the Community Paramedicine at Clinic (CP@clinic) Program to their community are presented. Key informant interviews took place between September 2020 and March 2021, and were thematically analysed using the Framework Hierarchical Analysis. There were seven themes that emerged with many subthemes: available services in the community, health care access, health challenges in community, causes of frailty, health care and community appreciations, community-specific benefits of CP@clinic, and CP@clinic program considerations for adaptation. CP@clinic program considerations for adaptation included defining the role of CP, refining referral processes to capture the target population, advertising and promoting, ensuring community awareness, determining clinic setting and composition, focusing on advocacy and timely continuity, adding to the program through time, managing resistance, engaging community and partners, deploying cultural training and language accommodations, leveraging community assets, and ensuring sustainability. Focusing on continuity, engagement, and leveraging available resources may support the success of the CP@clinic program implementation. Findings from this study may be useful to other underserved communities in Canada seeking health programming.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".