Cultural adaptation of a community‐based advance serious illness planning decision aid to the Quebec context involving end‐users
Bibliographic record
Abstract
INTRODUCTION: Traditional advance care planning focuses on end-of-life planning in the context of a certain or imminent death. It is not tailored for serious illness planning, where the 'death' outcome is uncertain. 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. A cultural adaptation was necessary to make the material suitable to the context of Quebec, a French-speaking Canadian province. METHODS: We engaged lay collaborators and experts in a panel, involving three phases of consultation and data collection. These included an online questionnaire, focused interviews and virtual focus groups that identified elements within the francophone PWG affecting its feasibility, adaptation and integration, as well as items that should be modified. RESULTS: We engaged 22 collaborators between April and September 2021. The majority (82%) ranked the first translation as good or very good; most (70%) stated that they would recommend the final adaptation. Both lay and expert panel members suggested simplifying the language and framing the tool better within the context of other advance medical planning processes in Quebec. Translation was considered in a cultural context; the challenges identified by the research team or by collaborators were addressed during the focus group. Examples of wording that required discussion include translating 'getting the medical care that's right for you' when referring to the PWG's goal. An equivalent expression in the French translation was believed to invoke religious associations. Using the term 'machines' to describe life-sustaining treatments was also deliberated. CONCLUSION: Our collaborative iterative adaptation process led to the first French advanced serious illness planning tool. How acceptable and user-friendly this French adaptation of the PWG is in various Canadian French-speaking environments requires further study. CONTRIBUTION: We organized a focus group inviting both lay collaborators and experts to contribute to the interpretation of the results of the previous phases. This choice allowed us to add more value to our results and to the final PWG in French.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".