Towards more effective public health programming for injection drug users : development, evaluation and application of the injection drug user quality of life scale
Bibliographic record
Abstract
Background. Little attention has been given to the assessment of quality of life (QOL) in injection drug users (IDUs). Some studies have suggested that existing measures are inadequate for use in IDUs. Objectives. The objectives were: (1) to develop and evaluate a QOL measure for IDUs, the Injection Drug User Quality of Life Scale (IDUQOL), (2) to describe the QOL of cocaine and heroin IDUs and identify its constituents and correlates, and (3) to describe the relation between the QOL of cocaine and heroin IDUs and the use of public health programs. Methods. The psychometric properties of the IDUQOL were assessed in 61 IDUs, 85% of whom were re-interviewed within 4-weeks. The Flanagan Quality of Life Scale was used to assess the criterion validity of the IDUQOL. The IDUQOL was subsequently applied in a study of 260 Montreal IDUs to identify their most important life areas. Associations between QOL and the use of public health programs and other correlates were assessed using multiple linear regression. Results. The IDUQOL had good psychometric properties: the test-retest reliability was within accepted standards (intraclass correlation coefficient = 0.71) and the concurrent criterion validity between the IDUQOL and the Flanagan was moderate (Pearson coefficient = 0.57). In the study of 260 Montreal IDUs, housing was the most frequently selected life area of cocaine IDUs. Heroin IDUs most frequently selected money and feeling good about yourself. Both cocaine and heroin IDUs were generally dissatisfied with how these life areas fared. QOL was significantly better for HIV positive IDUs and IDUs who used meal programs, and was worse for IDUs who attended shelters and emergency departments. No strong relations were found with needle exchange program use, methadone or other drug treatment. Conclusion. The IDUQOL appeared to be a conceptually clear and culturally relevant QOL instrument with good psychometric properties. Programs that address the life conditions of IDUs might be needed foremost to other initiatives. Understanding the constituents and correlates of the QOL of IDUs is important to the development of more effective programs to curb disease transmission, and improve the well-being of IDUs.
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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 | 0.030 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".