Advance care planning dashboard: quality indicators and usability testing
Bibliographic record
Abstract
OBJECTIVE: Advance care planning (ACP) and goals of care designation (GCD) performance indicators were developed and implemented across Alberta, Canada, and have been used to populate an electronic ACP/GCD dashboard. The study objective was to investigate whether users found the indicators and dashboard usable and acceptable. METHODS: This study employed a survey among a convenience sample of ACP/GCD community of practice members. The survey included questions on demographics, clinical practices and a validated usability questionnaire for the dashboard, System Usability Scale (SUS). RESULTS: Eighteen of 33 community of practice members (54.5%) answered the survey. Half of participants had a leadership or management role for ≥10 years. Most respondents (55.6%) had access to the ACP/GCD dashboard, and various ACP/GCD audit resources were used. Mean SUS was 70.83 (SD 19.72), which was above the threshold for acceptability (68). Approximately three-quarters of respondents (72.7%) found the indicators informative and meaningful for their practice, and over half (54.5%) were willing to use the dashboard and/or indicators to change their ACP/GCD practice. CONCLUSION: The nine indicators and dashboard were acceptable and usable for monitoring ACP/GCD performance. This set of indicators shows promise for describing and evaluating ACP/GCD uptake throughout a complex, multisector healthcare system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".