The development of theory-informed participant-centred interventions to maximise participant retention in randomised controlled trials
Bibliographic record
Abstract
BACKGROUND: A failure of clinical trials to retain participants can influence the trial findings and significantly impact the potential of the trial to influence clinical practice. Retention of participants involves people, often the trial participants themselves, performing a behaviour (e.g. returning a questionnaire or attending a follow-up clinic as part of the research). Most existing interventions that aim to improve the retention of trial participants fail to describe any theoretical basis for the potential effect (on behaviour) and also whether there was any patient and/or participant input during development. The aim of this study was to address these two problems by developing theory- informed, participant-centred, interventions to improve trial retention. METHODS: This study was informed by the Theoretical Domains Framework and Behaviour Change Techniques Taxonomy to match participant reported determinants of trial retention to theoretically informed behaviour change strategies. The prototype interventions were described and developed in a co-design workshop with trial participants. Acceptability and feasibility (guided by (by the Theoretical Framework of Acceptability) of two prioritised retention interventions was explored during a focus group involving a range of trial stakeholders (e.g. trial participants, trial managers, research nurses, trialists, research ethics committee members). Following focus group discussions stakeholders completed an intervention acceptability questionnaire. RESULTS: Eight trial participants contributed to the co-design of the retention interventions. Four behaviour change interventions were designed: (1) incentives and rewards for follow-up clinic attendance, (2) goal setting for improving questionnaire return, (3) participant self-monitoring to improve questionnaire return and/or clinic attendance, and (4) motivational information to improve questionnaire return and clinic attendance. Eighteen trial stakeholders discussed the two prioritised interventions. The motivational information intervention was deemed acceptable and considered straightforward to implement whilst the goal setting intervention was viewed as less clear and less acceptable. CONCLUSIONS: This is the first study to develop interventions to improve trial retention that are based on the accounts of trial participants and also conceptualised and developed as behaviour change interventions (to encourage attendance at trial research visit or return a trial questionnaire). Further testing of these interventions is required to assess effectiveness.
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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 | 0.255 | 0.600 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads 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".