Adapting the comprehensive assessment of acceptance and commitment therapy processes (CompACT) questionnaire for contextual relevance in Uganda: A comprehensive approach
Bibliographic record
Abstract
Abstract The global utility of acceptance and commitment therapy highlights the need for adapting measures that can effectively capture the richness of psychological flexibility. One such instrument is the Comprehensive Assessment of Acceptance and Commitment Therapy Processes (CompACT). We translated the CompACT into Luganda and adapted it for use in Uganda. The original CompACT was translated into the Luganda language and reviewed through a series of evaluations. Nine mental health professionals participated in one-on-one interviews, while a focus group of eight culturally competent laypersons provided further insights. Their feedback resulted in revisions to enhance the instrument’s clarity, relevance, acceptability and completeness. The revised version was then cognitively tested with n = 25 trainees at Makerere University. Input from these various groups was synthesized and triangulated to develop the final version. A total of 23 items were adapted to improve the comprehensibility and completeness of the scale. Overall, respondents deemed the tool clear and acceptable. This study highlights the importance of a rigorous adaptation process, including translation, expert review, cognitive testing and feedback triangulation, to ensure psychological measures remain valid and relevant across cultures. Such an approach ensures accuracy in diverse contexts and provides a model for adapting psychological instruments for non-Western populations.
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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 | 0.017 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".