Using evidence when planning for trial recruitment: An international perspective from time-poor trialists
Bibliographic record
Abstract
INTRODUCTION: Recruiting participants to trials is challenging. To date, research has focussed on improving recruitment once the trial is underway, rather than planning strategies to support it, e.g. developing trial information leaflets together with people like those to be recruited. We explored whether people involved with participant recruitment have explicit planning strategies; if so, how these are developed, and if not, what prevents effective planning. METHODS: Design: Individual qualitative semi-structured interviews. Data were analysed using a Framework approach, and themes linked through comparison of data within and across stakeholder groups. Participants: 23 international trialists (UK, Canada, South Africa, Italy, the Netherlands); 11 self-identifying as 'Designers'; those who design recruitment methods, and 12 self-identifying as 'Recruiters'; those who recruit participants. Interviewees' had recruitment experience spanning diverse interventions and clinical areas. Setting: Primary, secondary and tertiary-care sites involved in trials, academic institutions, and contract research organisations supporting pharmaceutical companies. RESULTS: To varying degrees, respondents had prospective strategies for recruitment. These were seldom based on rigorous evidence. When describing their recruitment planning experiences, interviewees identified a range of influences that they believe impacted success: The timing of recruitment strategy development relative to the trial start date, and who is responsible for recruitment planning.The methods used to develop trialists' recruitment strategy design and implementation skills, and when these skills are gained (i.e. before the trial or throughout).The perceived barriers and facilitators to successful recruitment planning; and how trialists modify practice when recruitment is poor. CONCLUSIONS: Respondents from all countries considered limited time and disproportionate approvals processes as major challenges to recruitment planning. Poor planning is a mistake that trialists live with throughout the trial. The experiences of our participants suggest that effective recruitment requires strategies to increase the time for trial planning, as well as access to easily implementable evidence-based strategies.
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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 | 0.478 | 0.435 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.024 | 0.061 |
| Scholarly communication | 0.044 | 0.044 |
| Open science | 0.008 | 0.042 |
| Research integrity | 0.026 | 0.050 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".