Efficacy of Advance Care Planning Videos for Patients: A Randomized Controlled Trial in Cancer, Heart, and Kidney Failure Outpatient Settings
Bibliographic record
Abstract
BACKGROUND: Patient videos about advance care planning (ACP; hereafter "Videos"), were developed to support uptake of provincial policy and address the complexity of patients' decision-making process. We evaluate self-administered ACP Videos, compare the studies' choice of outcomes, show correlations between the patients' ACP actions, and discuss implications for health care policy. OBJECTIVE: To test the efficacy of the Videos on patients' ACP/goals of care designation conversations with a health care provider. DESIGN, SETTING, AND PARTICIPANTS: Using a 2-arm, 1:1 randomized controlled trial, we recruited outpatients with a diagnosis of kidney failure, heart failure, metastatic lung, gastrointestinal, or gynecological cancer from 22 sites. Analysis followed the intention-to-treat principle. INTERVENTIONS: Videos describing the ACP process and illustrating the resuscitative, medical, and comfort levels of care. MAIN OUTCOMES AND MEASURES: The primary outcome was the proportion of participants who reported having an ACP/goals of care designation (GCD) conversation with a health care provider by 3 mo. Outcomes were measured using the Behaviours in Advance Care Planning and Actions Survey, an online survey capturing ACP attitudes, processes, and actions. RESULTS: = 0.032). Adjusted for the quality of conversations, there was no significant difference. CONCLUSIONS: Videos as stand-alone tools do not engage individuals in high-quality ACP. Pragmatic trials are necessary to evaluate their impact on downstream outcomes when integrated into intentional, comprehensive conversations with a health care provider. Considering the strong correlation between 2 activities (physicians discussing options, patients telling health care providers preferences), policy should focus on empowering patients to initiate these conversations.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".