Methodological challenges and issues of recruiting for mental health and substance use disorders trials in primary care
Bibliographic record
Abstract
Poor recruitment to controlled trials is a frequently reported problem. Challenges related to study design, communication, participants, interventions, outcomes, and clinician workload hinder recruitment, and the effectiveness of interventions used by trialists to increase recruitment rates is unknown. To explore the methodological challenges and issues in recruiting for mental health and substance use disorder trials in primary care, and to consider how these methodological challenges can be addressed. The presentation will recount the authors’ experience of recruiting for cluster randomized trials in primary care. Methodological challenges, such as clarity of instruction, patient characteristics, patient-doctor relationship, effects of intervention on patients and clinic, and personal benefits for clinicians will be described. The authors will consider how these might relate to and be used for peer learning and peer support in primary care research. The presentation will conclude with an overview of how lessons learned from past studies may be used to improve recruitment for trials of mental health and substance use disorders in primary care.
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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 | 0.831 | 0.910 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.009 | 0.009 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".