Promoting the internationalization of evidence‐based practice: Benchmarking as a strategy to evaluate culturally transported psychological treatments.
Bibliographic record
Abstract
In addition to the growing evidence-based practice movement in psychology, psychological \ntreatments are undergoing increasing adaptation and transportation to other countries and cultures around the world, prompting the need to evaluate treatments in these diverse settings. This article proposes the “benchmarking” strategy as a valuable approach to evaluate the effectiveness of culturally adapted and/or transported treatments and to promote the internationalization of evidence-based practice. We first describe the benchmarking concept in clinical research, followed by considerations for the cultural adaptation and transportation of psychological treatments. We then explain how the benchmarking strategy may be used to validate culturally transported and adapted psychological treatments. The article concludes with a discussion of considerations, limitations, and challenges for conducting cross-cultural benchmarking research.
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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 | 0.437 | 0.512 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".