Psychometric properties of the Arabic version of Quality of Life Index
Bibliographic record
Abstract
AIM: This paper reports a study to translate the English language version of the Quality of Life Index into Arabic and estimate its reliability and content validity. BACKGROUND: Quality of life has become an important concern in health care and social policy. It is a difficult construct to define and measure, as it is determined by cultural, ethical, personal and religious values. The generic Ferrans and Powers Quality of Life Index was developed to measure quality of life of healthy individuals. Specific versions of the index were developed for particular diseases, such as diabetes, cancer and end-stage kidney disease. The instruments were initially developed for English-speaking clients and were later translated into several languages and used within a variety of cultures. However, there were no Arabic versions of the tool available to measure the quality of life of general populations or of people with particular diseases. METHOD: The Quality of Life Index was translated into Arabic using two of the techniques suggested in the literature for translation - back translation and bilingual technique. The same process was followed in the translation of the original scale and various disease-specific versions of the instrument. The work took place between 1995 and 2004. FINDINGS: The translated Arabic Quality of Life Index demonstrated a high degree of accuracy of translation and estimates of content validity. Subsequent to the translation of the original scale into Arabic, 13 disease-related versions of the instrument were translated and are ready for use with clients who speak Arabic. Four of the versions have been used to collect data from clients. The results revealed high estimates of reliability for the generic, diabetes, cancer and dialysis versions. CONCLUSION: The Arabic version of the Quality of Life Index is highly reliable and has sufficient content validity for measuring quality of life of Arabic-speaking clients.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".