Translation and Adaptation of the Child and Youth Resilience Measure-Revised and Rugged Resilience Measure: A Mixed-Method Study Among Adolescents in Nepal
Bibliographic record
Abstract
Resilience, the capacity to adapt positively in adversity, is a key protective factor for adolescent well-being, particularly for depression and anxiety, which are highly prevalent among adolescents in Nepal. Accurate measurement across cultural contexts is essential to identify at-risk adolescents and understand protective mechanisms. This study culturally adapted and evaluated the psychometric properties of the Child and Youth Resilience Measure-Revised (CYRM-R) and Rugged Resilience Measure (RRM) in Nepal to ensure cultural relevance, reliability, and validity. This mixed-method study focused on poverty-affected adolescents in Kathmandu, using focus group discussions, cognitive interviews, pilot assessments, and a cross-sectional survey. The findings indicated Nepali versions of CYRM-R and RRM were acceptable, comprehensible, and relevant based on qualitative feedback. Most items showed item-total correlations between 0.2 and 0.5, indicating good discrimination, and internal consistency was satisfactory (α and ω > 0.7). Exploratory and confirmatory factor analyses supported a unidimensional structure, with an alternative two-factor solution explored for CYRM-R. Test-retest reliability was moderate overall, with some subscales less consistent. Both tools demonstrated strong psychometric properties, including face, content, convergent, and known-groups validity. The Nepali CYRM-R and RRM provide culturally robust tools for assessing adolescent resilience, supporting researchers, educators, and policymakers in designing targeted 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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".