Advance Care Planning: A Community Engagement Value Based Approach
Bibliographic record
Abstract
This workshop focuses on the design and ongoing implementation of an innovative advance care planning (ACP) initiative in one Canadian Health Authority. The initiative took a values based, community engagement approach to developing an ACP program in two communities. Developmental evaluation systematically informs the emerging programs. The initiative is in its second year and successes to date include: a shift in the relationship between the health authority and participating communities in response to a new way of ‘doing business’ where change is not mandated from the ‘top down’ but emerges ‘in the middle’ through active collaborative engagement; capacity development within both the health authority organizational team and the participating communities; a community specific vision of ACP that is broader than health care decision making; and, a comprehensive education program for ACP facilitators. This innovative approach has been challenging to enact in a system where a traditional policy based orientation to program development and outcomes based evaluation are the norm. Instead, success has been determined via real time decision making that aligns with the values framework underpinning the project. Multiple course corrections have occurred such as adopting developmental evaluation and moving away from an evaluative logic model. The responsive, values based process will be discussed, strengths and challenges identified and successes highlighted.
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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 | 0.036 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.023 | 0.008 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".