A New Scale for Assessing Benzodiazepine Use Motives Among Community and Clinical Samples: The Development and Validation of the MBUQ- 48
Bibliographic record
Abstract
Abstract The risk for non-medical use and dependence on benzodiazepines (BZDs) is high. However, there is no available validated psychometric instrument that assesses the motives for BZD use. Therefore, the aim of the present study was to develop a scale identifying the motives for BZD use, examine the factor structure, and corroborate the construct validity of the scale. Items for the scale were generated from previous data collection and from the empirical literature. Consequently, 82 motives were tested among a large community ( N = 1424) and a clinical sample ( N = 113). Medical and non-medical BZD use, other substance use, and several psychological constructs were assessed in both samples. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), as well as bivariate correlations and regression analyses, were performed. The EFA model included 48 items with four factors: “personal and interpersonal benefits”, “substance use regulation”, “coping”, and “sleep facilitation”. The four-factor CFA model demonstrated adequate levels of model fit. Members of the clinical sample had significantly higher rates of all four motives. The construct validity of the Motives for Benzodiazepine Use Questionnaire (MBUQ-48) was supported by positive correlations between the motivational factors and psychological constructs, different outcomes of BZD use, and other substance use. Coping motives had positive association with various outcomes of BZD use. Based on the results, the MBUQ-48 is a reliable and valid scale for assessing motives for BZD use. Exploring the motivations underlying BZD use can help clinicians in the recognition of the risk of BZD use disorder and in increasing the efficacy of therapeutic processes.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".