A guiding process to culturally adapt assessments for participation-focused pediatric practice: the case of the Participation and Environment Measures (PEM)
Bibliographic record
Abstract
PURPOSE: There is unprecedented opportunity to evaluate children's participation in diverse cultural contexts, to enhance cross-cultural research, advance the delivery of culturally responsive pediatric rehabilitation, and translate new knowledge on a global scale. The participation concept is complex and heavily influenced by a child's context. Therefore, effectively capturing the participation concept requires valid, reliable, and culturally sensitive participation-focused measures. This perspective paper proposes a structured process for culturally adapting measures of participation for children and youth with disabilities. METHODS: Elements of the Applied Cultural Equivalence Framework and Beaton and colleagues' six-step process were used to create a guiding process for culturally adapting a Participation and Environment Measure (PEM) while drawing on two distinct cultural contexts. This process included forward and back language translations, and semi-structured cognitive interviews, to develop adapted versions of the PEM that are ready for psychometric validation. RESULTS: Common challenges to culturally adapting PEM content and administration are identified and methodological strategies to mitigate these challenges are proposed. CONCLUSIONS: The proposed process can guide rehabilitation specialists and researchers in adapting participation measures that are suitable for their culture. Such a process can facilitate scalable implementation of evidence-based tools to support participation-based practice in the rehabilitation field.Implications for RehabilitationThe use of a systematic process can harmonize efforts by rehabilitation researchers and service providers to effectively culturally adapt pediatric participation measures to optimize its impact for culturally sensitive research and practice targeting participation.Two distinct, yet complementary, illustrative exemplars showcase the range of considerations and strategies, such as by conducting consecutive rounds of cognitive interviews, when teams use this systematic process to cultural adapt a pediatric participation measure.The systematic process outlined in this paper promotes rigor in achieving all elements of cultural equivalency, when feasible, to best ensure that the participation measure is suitable for use in the target cultural context.
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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 | 0.229 | 0.201 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".