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Record W2134483528 · doi:10.1186/1471-2288-14-46

Recruiting and motivating black subjects to complete a lengthy survey in a large cohort study: an exploration of different strategies

2014· article· en· W2134483528 on OpenAlexaboutno aff
Patti Herring, Terry Butler, Sonĵa E. Hall, Hannelore Bennett, Susanne Montgomery, Gary E. Fraser

Bibliographic record

VenueBMC Medical Research Methodology · 2014
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsnot available
FundersNational Cancer Institute
KeywordsCohortIncentiveFlexibility (engineering)TournamentCohort studyMedicineRandomized controlled trialPsychologyFamily medicineGerontologyMedical educationEconomicsSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: The effectiveness of multiple innovative recruitment strategies for enrolling Black/African American participants to the Adventist Health Study-2 (AHS-2) is described. The study's focus is diet and breast, prostate and colon cancer. METHODS: Promotions centered on trust, relationship building and incentives for increasing enrollment and questionnaire return rate. Of the sub-studies described, one had a randomized control group, and the others, informal controls. The subjects are from all states of the U.S. and some provinces of Canada. The offer of a Black art piece, follow-up calls, a competitive tournament as well as other strategies accounted for nearly 3,000 additional returns even though they were often used in small subsets. RESULTS: Flexibility and multiple strategies proved advantageous in gaining the cooperation of Blacks, who are usually reluctant to participate in research studies. CONCLUSIONS: Lessons learned during initial enrollment should help us retain our final Black cohort of 26,000, and obtain new information when required.

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)0.282
metaresearch head score (Gemma)0.748
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.466
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.2820.748
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0000.000

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 head0.935
GPT teacher head0.699
Teacher spread0.236 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations7
Published2014
Admission routes1
Has abstractyes

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