AB015. Developing community palliative care programs using a capacity building approach: the Northwestern Ontario experience
Bibliographic record
Abstract
Northwestern Ontario, Canada, is a large, sparsely populated geographic area with many small rural and remote communities. Most health services are delivered by primary care generalists. The goal of the North West LHIN Regional Palliative Care Program (RPCP) with St. Joseph's Care Group is to create an integrated system of palliative care accessible to all individuals in Northwestern Ontario who would benefit from a palliative approach, regardless of location, prognosis, or diagnosis. To achieve this goal and increase access to palliative care at the primary care level, the RPCP has used the Kelley Community Capacity Development Model to guide their process of developing palliative care programs in 8 different rural communities. In each community, a local community facilitator is identified and a palliative care committee is established. Subsequently, other community members and health care providers with an interest in death, dying, loss, and bereavement are engaged in a local workshop to discuss their community's strengths, identify areas of improvement, and develop a plan to implement their local palliative care program. Throughout, the RPCP team acts as a resource for the Kelley capacity development process, introduces tools and resources that have been created to support this work, and shares lessons learned from other communities. At the conference, rural community facilitators will share their experiences engaging their communities and highlight accomplishments in developing palliative care. Participants will discover how the Kelley community capacity building approach can mobilize rural, remote, and northern communities and improve access to a palliative approach to care. The North West LHIN RPCP has used the Kelley Community Capacity Development Model to guide development of palliative care programs in eight different rural communities. This workshop will describe the process, introduce tools that have been created to support this work, and share lessons learned from 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.018 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".