Injection Drug Use Quality of Life scale (IDUQOL): A validation study
Bibliographic record
Abstract
BACKGROUND: Existing measures of injection drug users' quality of life have focused primarily on health and health-related factors. Clearly, however, quality of life among injection drug users is impacted by a range of unique cultural, socioeconomic, medical, and geographic factors that must also be considered in any measure. The Injection Drug User Quality of Life (IDUQOL) scale was designed to capture the unique and individual circumstances that determine quality of life among injection drug users. The overall purpose of the present study was to examine the validity of inferences made from the IDUQOL by examining the (a) dimensionality, (b) reliability of scores, (c) criterion-related validity evidence, and (d) both convergent and discriminant validity evidence. METHODS: An exploratory factor analysis using principal axis factoring in SPSS 12.0 was conducted to determine whether the use of a total score on the IDUQOL was advisable. Reliability of scores from the IDUQOL was obtained using internal consistency and one-week test-retest reliability estimates. Criterion-related validity evidence was gathered using variables such as stability of housing, sex trade involvement, high-risk injection behaviours, involvement in treatment programs, emergency treatment or overdose over the previous six months, hospitalization and emergency treatment over the subsequent six month period post data collection. Convergent and discriminant validity evidence was gathered using measures of life satisfaction, self-esteem, and social desirability. RESULTS: The sample consisted of 241 injection drug users ranging in age from 19 to 61 years. Factor analysis supports the use of a total score. Both internal consistency (alpha = .88) and one-week test-retest reliability (r = .78) for IDUQOL total scores were good. Criterion-related, convergent, and discriminant validity evidence supports the interpretation of IDUQOL total scores as measuring a construct consistent with quality of life. CONCLUSION: The findings from this study provide initial evidence to support the use of the IDUQOL total score. The results of the study also suggest the IDUQOL could be further strengthened with additional attention to how some IDUQOL domains are described and satisfaction is measured.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".