Evaluation of a French adaptation of a community-based advance serious illness planning decision aid
Bibliographic record
Abstract
Objective: The Plan Well Guide™ (PWG) is a decision aid that empowers lay persons to better understand different types of care and prepares them, and their substitute decision-makers, to express both their authentic values and informed treatment preferences in anticipation of serious illness. We aimed to determine the acceptability of the newly translated French PWG and to evaluate decisional readiness and decisional conflict following its use by lay people. Methods: This is an acceptability and exploratory outcomes evaluation.Participants were requested to read and complete the French PWG and to engage in an online interview. We used the Acceptability Scale to determine the acceptability and the Preparation for Decision-making Scale and decisional conflict Scale to evaluate decisional readiness. Results: Forty-two (42) people participated. The average score on the Acceptability Scale was 18.1 (scale range: 4-20 [high-better]) and 26.6 on the Preparation for Decision-Making Scale (scale range: 6-30 [high-better]). A significant number of respondents reported needing more support to help them make better decisions. Conclusion: The French PWG has been deemed acceptable and relevant for lay people not currently facing clinical decisions. Innovation: The Plan Well Guide is innovative as it is the first decision aid empowering lay people for advance serious illness planning.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.006 | 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".