Cultural adaptation and validation of patient decision aids: a scoping review
Bibliographic record
Abstract
In order to promote self-determination, patients have to be actively involved with their care providers in health-care decision making, especially when such decisions involve personal preferences. Decision aids (DAs) are tools that can contribute to patient-centered decision-making processes. To benefit from previous fieldwork and avoid duplicating developmental efforts and producing many similar DAs, the adaptation of existing DAs to new cultural contexts is a resource-saving option. However, there are no guidelines on how to culturally adapt and validate DAs. This study aimed to identify and document existing procedures for the cultural adaptation and validation of patient DAs. A scoping review examined studies conducting cultural adaptation and/or validation of patient DAs. The following databases were searched in February 2016: CINAHL, EMBASE, Medline (Ovid), PASCAL, PsychINFO, and PubMed. From the 13 studies selected, 11 main procedures were identified: appraisal of the original DA, assessment of the new cultural context, translation, linguistic adaptation, cultural adaptation, usability testing, exploration of DA acceptability, test-retest reliability, content validity, construct validity, and criterion validity. A conceptual synthesis of these studies suggests there are four phases in the adaptation/validation process of DAs aimed at: 1) exploring the original DA and the new cultural context, 2) adapting the original DA to the new cultural context, 3) lab testing the preliminary version of the adapted DA, and 4) field testing the adapted DA in a real use context. By facilitating the adaptation and broader implementation of DAs, patients may ultimately be empowered in decision-making processes.
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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 | 0.132 | 0.386 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.034 | 0.032 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".