Development and validation of a theory-based questionnaire examining barriers and facilitators to discontinuing long-term benzodiazepine receptor agonist use
Bibliographic record
Abstract
Abstract Background Long-term use of benzodiazepine receptor agonists (BZRAs) is a persistent healthcare challenge and poses patient safety risks. Interventions underpinned by behaviour change theory are needed to support discontinuation of long-term BZRA use. The aim of this study was to develop and validate a questionnaire based on the Theoretical Domains Framework (TDF) to examine mediators of behaviour change relating to the discontinuation of long-term BZRA use. Methods An initial 52 item questionnaire was developed using the 14 domains of TDF version 2 and iteratively refined over two rounds. The questionnaire was disseminated online via online support groups that focused on BZRAs to community-based adults with either current or previous experience of taking BZRAs on a long-term basis (≥ 3 months). Confirmatory factor analysis was undertaken to assess the questionnaire’s reliability, discriminant validity and goodness of fit. The Standardized Root Mean Square Residual (SRMR), Root Mean Square Error of Approximation (RMSEA) and Comparative Fit Index (CFI) were calculated. Results Following an interactive process of adjustment, the results obtained from confirmatory factor analysis resulted in the final questionnaire consisting of 29 items across nine theoretical domains. The internal consistency reliability values across these domains ranged from 0.62 to 0.85. For the final model, the SRMR was 0.23, the RMSEA was 0.11 and the CFI was 0.6. Conclusions The questionnaire offers a potential tool that could be used to identify domains that need to be targeted as part of a behaviour change intervention at an individual patient level. Further research is needed to assess the questionnaire’s acceptability and usability, and to develop a scoring system so that domains can be prioritised and subsequently targeted as part of an intervention.
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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 | 0.023 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".