Can a patient-directed video improve inpatient advance care planning? A prospective pre-post cohort study
Bibliographic record
Abstract
BACKGROUND: Patients and their families often have an inadequate understanding of the risks and benefits of their advance care planning (ACP) options. Improving patients' knowledge of therapeutic interventions allows them to better select treatments they believe are most appropriate for their condition. OBJECTIVES: To determine if a video aimed at educating and engaging hospitalised patients on a standardised ACP order set can improve (1) inpatient understanding of key ACP concepts, (2) ACP documentation within 48 hours of hospital admission, (3) concordance between a patient's expressed and chart-documented care preferences, (4) patient satisfaction with decision-making, and (5) patient's decisional confidence. METHODS: A prospective, non-randomised, pre-post intervention study of 252 inpatients in a 215-bed community-based hospital in Comox, British Columbia, Canada. RESULTS: Our video decision support tool was associated with significant improvements in (1) patient understanding of key ACP concepts (70%-100%; p<0.0001), (2) ACP documentation within 48 hours of hospital admission (81%-92%; p=0.01), (3) concordance between patients' expressed wishes and chart documentation (69%-89%; p<0.0001), (4) patient satisfaction with decision-making (Canadian Health Care Evaluation Project Lite score: 4.3-4.5, p=0.001), and (5) patient's decisional confidence (patients with no decisional conflict, increased from 72% to 93%; p<0.0001). CONCLUSION: A 13 min video aimed at educating and engaging inpatients on ACP concepts improved patient understanding of key ACP concepts, rates of ACP documentation and patient satisfaction with decision-making.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".