Describing the content of trial recruitment interventions using the TIDieR reporting checklist: a systematic methodology review
Bibliographic record
Abstract
BACKGROUND: Recruiting participants to clinical trials is an ongoing challenge, and relatively little is known about what recruitment strategies lead to better recruitment. Recruitment interventions can be considered complex interventions, often involving multiple components, targeting a variety of groups, and tailoring to different groups. We used the Template for Intervention Description and Replication (TIDieR) reporting checklist (which comprises 12 items recommended for reporting complex interventions) to guide the assessment of how recruitment interventions are described. We aimed to (1) examine to what extent we could identify information about each TIDieR item within recruitment intervention studies, and (2) observe additional detail for each item to describe useful variation among these studies. METHODS: We identified randomized, nested recruitment intervention studies providing recruitment or willingness to participate rates from two sources: a Cochrane review of trials evaluating strategies to improve recruitment to randomized trials, and the Online Resource for Research in Clinical triAls database. First, we assessed to what extent authors reported information about each TIDieR item. Second, we developed descriptive categorical variables for 7 TIDieR items and extracting relevant quotes for the other 5 items. RESULTS: We assessed 122 recruitment intervention studies. We were able to extract information relevant to most TIDieR items (e.g., brief rationale, materials, procedure) with the exception of a few items that were only rarely reported (e.g., tailoring, modifications, planned/actual fidelity). The descriptive variables provided a useful overview of study characteristics, with most studies using various forms of informational interventions (55%) delivered at a single time point (90%), often by a member of the research team (59%) in a clinical care setting (41%). CONCLUSIONS: Our TIDieR-based variables provide a useful description of the core elements of complex trial recruitment interventions. Recruitment intervention studies report core elements of complex interventions variably; some process elements (e.g., mode of delivery, location) are almost always described, while others (e.g., duration, fidelity) are reported infrequently, with little indication of a reason for their absence. Future research should explore whether these TIDieR-based variables can form the basis of an approach to better reporting of elements of successful recruitment interventions.
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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 | 0.623 | 0.952 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.008 |
| 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".