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Record W3007889882 · doi:10.2196/15079

Recommendations From a Descriptive Evaluation to Improve Screening Procedures for Web-Based Studies With Couples: Cross-Sectional Study

2020· article· en· W3007889882 on OpenAlexvenueno aff
Jason W. Mitchell, Tanaka Chavanduka, Stephen Sullivan, Rob Stephenson

Bibliographic record

VenueJMIR Public Health and Surveillance · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSurvey Methodology and Nonresponse
Canadian institutionsnot available
FundersNational Institute of Mental Health
KeywordsCross-sectional studyDescriptive researchEnvironmental healthComputer scienceMedicinePsychologyStatisticsMathematics

Abstract

fetched live from OpenAlex

BACKGROUND: Although there are a number of advantages to using the internet to recruit and enroll participants into Web-based research studies, these advantages hinge on data validity. In response to this concern, researchers have provided recommendations for how best to screen for fraudulent survey entries and to handle potentially invalid responses. Yet, the majority of this previous work focuses on screening (ie, verification that individual met the inclusion criteria) and validating data from 1 individual, and not from 2 people who are in a dyadic relationship with one another (eg, same-sex male couple; mother and daughter). Although many of the same data validation and screening recommendations for Web-based studies with individual participants can be used with dyads, there are differences and challenges that need to be considered. OBJECTIVE: This paper aimed to describe the methods used to verify and validate couples' relationships and data from a Web-based research study, as well as the associated lessons learned for application toward future Web-based studies involving the screening and enrollment of couples with dyadic data collection. METHODS: We conducted a descriptive evaluation of the procedures and associated benchmarks (ie, decision rules) used to verify couples' relationships and validate whether data uniquely came from each partner of the couple. Data came from a large convenience sample of same-sex male couples in the United States, who were recruited through social media venues for a Web-based, mixed methods HIV prevention research study. RESULTS: Among the 3815 individuals who initiated eligibility screening, 1536 paired individuals (ie, data from both partners of a dyad) were assessed for relationship verification; all passed this benchmark. For data validation, 450 paired individuals (225 dyads) were identified as fraudulent and failed this benchmark, resulting in a total sample size of 1086 paired participants representing 543 same-sex male couples who were enrolled. The lessons learned from the procedures used to screen couples for this Web-based research study have led us to identify and describe four areas that warrant careful attention: (1) creation of new and replacement of certain relationship verification items, (2) identification of resources needed relative to using a manual or electronic approach for screening, (3) examination of approaches to link and identify both partners of the couple, and (4) handling of bots. CONCLUSIONS: The screening items and associated rules used to verify and validate couples' relationships and data worked yet required extensive resources to implement. New or updating some items to verify a couple's relationship may be beneficial for future studies. The procedures used to link and identify whether both partners were coupled also worked, yet they call into question whether new approaches are possible to help increase linkage, suggesting the need for further inquiry.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.725
metaresearch head score (Gemma)0.783
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.275
Threshold uncertainty score0.339

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.7250.783
Meta-epidemiology (narrow)0.0030.004
Meta-epidemiology (broad)0.0050.010
Bibliometrics0.0130.012
Science and technology studies0.0060.005
Scholarly communication0.0120.016
Open science0.0090.006
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.0080.005

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 head0.422
GPT teacher head0.512
Teacher spread0.090 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designObservational
DomainMethods
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

Citations26
Published2020
Admission routes1
Has abstractyes

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