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Record W4394576834 · doi:10.1186/s12874-024-02195-5

Describing the content of trial recruitment interventions using the TIDieR reporting checklist: a systematic methodology review

2024· article· en· W4394576834 on OpenAlexafffund
Natasha Hudek, Kelly Carroll, Seana N. Semchishen, Shelley Vanderhout, Justin Presseau, Jeremy Grimshaw, Dean Fergusson, Katie Gillies, Ian D. Graham, Monica Taljaard

Bibliographic record

VenueBMC Medical Research Methodology · 2024
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsUniversity of OttawaOttawa Hospital
FundersMedical Research CouncilCanadian Institutes of Health Research
KeywordsChecklistPsychological interventionSystematic reviewFidelityRandomized controlled trialCategorical variableMEDLINEResearch designIntervention (counseling)Descriptive statisticsClinical trialComputer scienceMedicinePsychologyStatisticsNursingPathology

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.623
metaresearch head score (Gemma)0.952
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.686
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.6230.952
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.002
Science and technology studies0.0000.004
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.008
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.996
GPT teacher head0.798
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designSystematic review
DomainMethods
GenreMethods

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".

Quick stats

Citations13
Published2024
Admission routes2
Has abstractyes

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