Safe and valid? A systematic review of the psychometric properties of culturally adapted depression scales for use among Indigenous populations
Bibliographic record
Abstract
Background: Implementing culturally sensitive psychometric measures of depression may be an effective strategy to improve acceptance, response rate, and reliability of psychological assessment among Indigenous populations. However, the psychometric properties of depression scales after cultural adaptation remain unclear. Methods: We screened the Ovid Medline, PubMed, Embase, Global Health, PsycInfo, and CINAHL databases through three levels of search terms: Depression, Psychometrics, and Indigenous, following the PRISMA guidelines. We assessed metrics for reliability (including Cronbach's alpha), validity (including fit indices), and clinical utility (including predictive value). Results: Across 31 studies included the review, 13 different depression scales were adapted through language or content modification. Sample populations included Indigenous from the Americas, Asia, Africa, and Oceania. Most cultural adaptations had strong psychometric properties; however, few and inconsistent properties were reported. Where available, alphas, inter-rater and test-retest reliability, construct validity, and incremental validity often indicated increased cultural sensitivity of adapted scales. There were mixed results for clinical utility, criterion validity, cross-cultural validity, sensitivity, specificity, area under the receiver operating characteristic curve, predictive value, and likelihood ratio. Conclusions: Modifications to increase cultural relevance have the potential to improve fit and acceptance of a scale by the Indigenous population, however, these changes may decrease specificity and negative predictive value. There is an urgent need for suitable tools that are useful and reliable for identifying Indigenous individuals for clinical treatment of depression. This awaits future work for optimal specificity and validated cut-off points that take into account the high prevalence of depression in these populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".