How to evaluate an advance care planning policy – strategies for each stage of implementation
Bibliographic record
Abstract
In 2008, the “Advance Care Planning: Goals of Care Designation (Adult)” policy (ACP:GCD) was systematically implemented throughout the Calgary Zone of Alberta Health Services. A comprehensive evaluation plan was imbedded pre and post implementation across all health sectors. Key indicators were identified to measure policy outcomes. A diverse and flexible evaluation model was developed for differing stages of implementation. Evaluation successes and challenges will be outlined. The outcomes achieved demonstrate the successes of a comprehensive ACP policy framework. Our evaluation approach was critical to ensuring full policy integration. A large scale comprehensive chart audit was conducted across all sectors at baseline, 6 and 18 months post implementation; additional charts were reviewed as needed (total of 18669 charts). Key outcomes relating to policy implementation, transfer of information, and end of life care preferences being followed were achieved in all sectors. At 18 months, 4 out of 5 sectors had GCDs on at least 93% of their charts. GCDs transferred from acute care to facility living 84% of the time. 97% of GCDs were followed at end of life. Outcome data informed strategies for ongoing integration of the policy, areas for continued quality improvement and gaining and sustaining organisational support. Three years post implementation, a different approach to evaluation is required. Although data demonstrates increasing adherence to policy principles, areas for continued improvement were also identified. We will present targeted quality improvement activities being used to address gaps and barriers.
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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 | 0.292 | 0.358 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.029 | 0.025 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".