147 Successful Strategies Supporting Recruitment, Intervention Delivery and Retention Targets in a Randomized Controlled Trial of a Complex Intervention
Bibliographic record
Abstract
Background: Complex interventions are widely used in modern health care practice and are defined as those having potentially interacting components. Evaluation can be challenging due to difficulties in logistics, standardisation and delivery. In addition, there can be difficulty recruiting to time and to target (particularly in multicentre studies) and minimising attrition and data loss. We report how the RAFT study [a seven-centre randomized controlled trial (RCT) comparing a complex group cognitive-behavioural (CB) intervention with standard care for the reduction of fatigue impact in patients with RA) is implementing successful strategies to meet recruitment, intervention delivery and retention targets. The study requires patients to make a substantial commitment over a 2 year period and for the intervention to be delivered by routine clinical staff trained for this purpose. Methods: The following strategies were agreed upon during the planning and design phase: Maximising recruitment: Funded research nurse time at all seven sites; mailshot option for approach; recruitment posters for clinics; flexible and pragmatic approach to session attendance; telephone, email and postal contact; newsletter and regular knowledge exchange between the central trial management team and sites; weekly recruitment updates and reviews. Ensuring intervention delivery: Flexible course dates and times set by each site, regular communication with the central management team to discuss foreseeable issues and preventative actions, provision of real-time clinical supervision and full-time telephone/email support. Minimising attrition and data loss: Primary outcome collection by telephone, ensuring regular personal contact; secondary data collection by postal questionnaire, reducing the number of hospital visits; telephone reminders; partial withdrawal options; personalized letters and thank you cards. Patient involvement: We had a number of acceptability and feasibility consultations with our two patient partners. Both partners had prior experience attending the intervention, were co-applicants on the grant proposal and continue to provide a patient perspective as members of the trial management group.
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How this classification was reachedexpand
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 | 0.012 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".