BOS2c.001 Measuring the effectiveness of a compassionate communities approach to raise public awareness of advance care planning: an evaluation framework
Bibliographic record
Abstract
Background Raising awareness of advance care planning is essential for better preparing people for living with serious illness. A multi-year, multi-sectoral initiative is underway in Alberta, Canada (population 4.4 million) to increase public awareness and understanding of advance care planning using a Compassionate Communities approach. Here we describe the development of an evaluation framework to: 1) determine effectiveness of the initiative in raising public awareness of advance care planning, and 2) contribute to best practice and knowledge on evaluating a Compassionate Communities-based public awareness initiative. Methods A literature review was undertaken to identify relevant framework(s) to guide our evaluation. Meetings were held with stakeholders to solicit feedback on the selected evaluation framework(s) and proposed measures. Results A logic model was developed to synthesize the goals, inputs, audience, activities, outputs, process measures and outcome measures for the program. Process evaluation is structured around the Healthy End of Life Program Evaluation Framework, based on its public health palliative care approach to evaluation, health promotion principles, and community development approach. Outcome evaluation is structured around the Australia Palliative Care Evaluation framework to capture impacts on ‘consumers’ (i.e. ‘the public’, patients, families, carers, friends), ‘providers’ (i.e. professionals, volunteers, community organizations) and the ‘broader care delivery system’ (i.e. structures and processes, networks, relationships). Proposed process and outcome measures were refined with stakeholder input. Conclusions Preliminary data collection is proving feasible and meaningful in measuring the impacts of this Compassionate Communities-based public awareness initiative on individuals, communities, and systems of care.
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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 | 0.135 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
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".