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 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 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".