Examining Changes in Quality of Life as an Outcome Measure in Three Randomized Controlled Trials of Online Interventions That Included an Intervention for Hazardous Alcohol Use
Bibliographic record
Abstract
Background Quality of life (QOL) summarizes an individual’s perceived satisfaction across multiple life domains. Many factors can impact this measure, but research has demonstrated that individuals with addictions, physical, and mental health concerns tend to score lower than general population samples. While QOL is often important to individuals, it is rarely used by researchers as an outcome measure when evaluating treatment efficacy.Methods This secondary analysis used data collected during three separate randomized controlled trials testing the efficacy of different online interventions to explore change in QOL over time between treatment conditions. The first project was concerned with only alcohol interventions. The other two combined either a gambling or mental health intervention with a brief alcohol intervention. Males and females were analyzed separately.Results This analysis found treatment effects among female participants in two projects. In the project only concerning alcohol, female quality of life improved more among those who received an extensive intervention for hazardous alcohol use compared to a brief intervention (p = .029). QOL among females who received only the mental health intervention improved more than those who also received a brief alcohol intervention (p = .049).Conclusion Poor QOL is often cited as a reason individuals decide to make behavior changes, yet treatment evaluations do not typically consider this patient-important outcome. This analysis found some support for different treatment effects on QOL scores in studies involving at least one intervention for hazardous alcohol use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.034 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".