Developing the Community Paramedicine Needs Assessment Tool
Bibliographic record
Abstract
Background/Objectives: Community paramedicine programs have existed since the early 2000s, and while resource optimization remains a predominant driver, innovation in recent years demonstrates that when community paramedicine is integrated into healthcare, it is well-positioned to support the needs of structurally marginalized communities by focusing services for those facing barriers to accessing equitable care. A recent scoping review described the evolving ways community paramedicine models are addressing health and social needs within communities around the world. We aimed to identify and explore existing community needs assessment tools in Canada to guide the initial development of a needs assessment tool for community paramedicine. Methods: We conducted a document analysis of existing community needs assessment resources to identify current tools or processes used to identify community needs, as well as determine gaps to address and support. Documents were collected for review via a targeted literature search of both published and gray sources, and direct document requests of community paramedicine service providers to review guides informing current service planning in Canada. We presented a draft of the tool to participants at a community paramedicine conference for their review and feedback, and we incorporated this feedback into the final version. Results: We reviewed 38 documents to identify and synthesize key elements within community health and social needs assessment tools and frameworks. Findings informed an interim Community Paramedicine Needs Assessment Tool (CPNAT) that the team presented to 112 community paramedicine experts and partners. We received 33 group responses of detailed feedback that we used to further refine and finalize the tool. Conclusions: The CPNAT can support enhanced health equity by guiding community paramedicine programs to better align services, policies, and funding with the health and social care needs of communities.
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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 | 0.069 | 0.157 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.019 | 0.013 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".