Adding quality to quantity in randomized controlled trials of addiction prevention and treatment: a new framework to facilitate the integration of qualitative research
Bibliographic record
Abstract
BACKGROUND AND AIMS: Randomized controlled trials (RCTs) are important for evaluating interventions, and qualitative research is increasingly recognized as being crucial to the success of this enterprise. We aimed to describe and demonstrate a temporal parallel purpose framework to help researchers understand how to make optimum use of qualitative research before, during and after RCTs. This framework sets out specific rationales for conducting qualitative research at each stage of a trial, where the rationales presented relate to both the intervention and evaluation methodology. METHOD AND RESULTS: We conducted a scoping review of published qualitative studies undertaken alongside RCTs focusing on illicit drug use. We then used the temporal parallel purpose framework to present key findings to demonstrate how qualitative studies can add value to addiction RCTs by enhancing understanding of the intervention being trialled and/or the RCT itself. In so doing, we highlight the missed opportunities for addiction science when qualitative research is overlooked. We also explain why barriers to combining qualitative research and RCTs are neither inevitable nor insurmountable. CONCLUSIONS: The temporal parallel purpose framework provides a tool for assessing when and why to combine qualitative research with addiction treatment and prevention RCTs. Our paper and framework can help researchers formulate key questions that qualitative research can address. This can potentially save resources by reducing the number of poorly designed interventions and trials and prevent morbidity, mortality, and other addiction-related harms by facilitating the identification and implementation of interventions that are most likely to be effective.
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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 | 0.834 | 0.806 |
| Meta-epidemiology (narrow) | 0.006 | 0.008 |
| Meta-epidemiology (broad) | 0.013 | 0.014 |
| Bibliometrics | 0.034 | 0.019 |
| Science and technology studies | 0.013 | 0.088 |
| Scholarly communication | 0.031 | 0.053 |
| Open science | 0.014 | 0.039 |
| Research integrity | 0.018 | 0.025 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".