The development of a cultural validity assessment tool for First Nations people
Bibliographic record
Abstract
BACKGROUND: There is a paucity of quality appraisal tools specific to determine cultural validity. Cultural validity measures the appropriateness and applicability of a construct for a specific cultural group. It is often discussed in reference to determining if a construct developed in one cultural group is applicable, meaningful and equivalent in another cultural group. First Nations people conceptualise mental ill-health in vastly different ways than the biomedical models most used. Thus, research that does not consider cultural validity can have harmful effects. A specific tool to assess cultural validity in First Nations communities is required to address this significant gap in the literature. METHOD: The First Nations Cultural Validity Assessment Tool was developed to assess cultural validity in a meaningful way for First Nations people in Australia. The tool was designed by First Nations researchers with guidance from cultural and lived experience experts and pilot-tested by clinicians and researchers. RESULTS: The First Nations Cultural Validity Assessment Tool includes 10 criteria within three overarching factors (Psychometric properties, Cultural Psychometric properties and Cultural competency of staff/ethics). The First Nations Cultural Validity Assessment Tool is scored from 0 to 15, with higher scores indicating greater cultural validity. Pilot testing demonstrated excellent inter-rater reliability between scorers. CONCLUSION: This is the first tool to assess the cultural validity of measurement tools from the perspective of First Nations frameworks. The First Nations Cultural Validity Assessment Tool prioritises First Nations research values using a methodological approach that is acceptable within both non-Indigenous and Indigenous research practices.
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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 | 0.140 | 0.314 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| 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".