Implementing the Integrated Strategy for the Cultural Adaptation of Evidence-Based Interventions: An Illustration
Bibliographic record
Abstract
BACKGROUND: Persons' cultural beliefs about a health problem can affect their perceived acceptability of evidence-based interventions, undermining evidence-based interventions' adherence, and uptake to manage the problem. Cultural adaptation has the potential to enhance the acceptability, uptake, and adherence to evidence-based interventions. PURPOSE: To illustrate the implementation of the first two phases of the integrated strategy for cultural adaptation by examining Chinese Canadians' perceptions of chronic insomnia and evidence-based behavioral therapies for insomnia. METHODS: Chinese Canadians ( n = 14) with chronic insomnia attended a group session during which they completed established instruments measuring beliefs about sleep and insomnia, and their perceptions of factors that contribute to chronic insomnia. Participants rated the acceptability of evidence-based behavioral therapies and discussed their cultural perspectives regarding chronic insomnia and its treatment. RESULTS: Participants actively engaged in the activities planned for the first two phases of the integrated strategy and identified the most significant factor contributing to chronic insomnia and the evidence-based intervention most acceptable for their cultural group. CONCLUSIONS: The protocol for implementing the two phases of the integrated strategy for cultural adaptation of evidence-based interventions was feasible, acceptable, and useful in identifying culturally relevant evidence-based interventions.
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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 | 0.083 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".