Methodological challenges and issues of recruiting for mental health and substance use disorders trials in primary care
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.155 | 0.692 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it