Adequacy of the WHOQoL-BREF to Assess the Quality of Life of Victims of Armed Conflicts
Bibliographic record
Abstract
There is a deterioration in the quality of life (QoL) of survivor victims of warlike conflicts. Because there is a need to guarantee the effectiveness of assessment tools for these populations, we studied the adequacy of the World Health Organization Quality of Life Questionnaire (WHOQoL-BREF) to assess the QoL of 1,136 surviving victims of the armed conflict in Colombia. Although this questionnaire has yielded promising results, questions remain about its psychometric suitability for specific populations. We used model modification at the item level, comparisons of models with different factor structures, and dimensionality analysis to address the psychometric problems encountered. Dimensionality analysis using a bifactor model suggests that WHOQoL-BREF total scores might be a more appropriate way of reporting results when model fit adequacy is not reached. Conclusions are offered on the psychometric properties of the WHOQoL-BREF, the evaluation of special populations, and possible strategies to address future questionnaire modifications.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".