Cultural adaptation and validation of patient decision aids: a scoping review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it