Effect of “Speak Up” educational tools to engage patients in advance care planning in outpatient healthcare settings: A prospective before-after study
Bibliographic record
Abstract
BACKGROUND: Tools for advance care planning (ACP) are advocated to help ensure patient values guide healthcare decisions. Evaluation of the effect of tools introduced to patients in clinical settings is needed. OBJECTIVE: To evaluate the effect of the Canadian Speak Up Campaign tools on engagement in advance care planning (ACP), with patients attending outpatient clinics. Patient involvement: Patients were not involved in the problem definition or solution selection in this study but members of the public were involved in development of tools. The measurement of impacts involved patients. METHODS: This was a prospective pre-post study in 15 primary care and two outpatient cancer clinics. The outcome was scores on an Advance Care Planning Engagement Survey measuring Behavior Change Process on 5-point scales and Actions (0-21-point scale) administered before and six weeks after using a tool, with reminders at two or four weeks. RESULTS: 177 of 220 patients (81%) completed the study (mean 68 years of age, 16% had cancer). Mean Behavior Change Process scores were 2.9 at baseline and 3.5 at follow-up (mean change 0.6, 95% confidence interval 0.5 to 0.7; large effect size of 0.8). Mean Action Measure score was 3.7 at baseline and 4.8 at follow-up (mean change 1.1, 95% confidence interval 0.6-1.5; small effect size of 0.2). PRACTICAL VALUE: Publicly available ACP tools may have utility in clinical settings to initiate ACP among patients. More time and motivation may be required to stimulate changes in patient behaviors related to ACP.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".