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Record W4393953952 · doi:10.2196/56218

Lessons Learned From a Sequential Mixed-Mode Survey Design to Recruit and Collect Data From Case-Control Study Participants: Formative Evaluation

2024· article· en· W4393953952 on OpenAlexvenueno aff
Amanda D Tran, Alice E. White, Michelle Torok, Rachel H. Jervis, Bernadette Albanese, Elaine Scallan Walter

Bibliographic record

VenueJMIR Formative Research · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSurvey Methodology and Nonresponse
Canadian institutionsnot available
FundersCenters for Disease Control and PreventionColorado Department of Public Health and Environment
KeywordsContext (archaeology)Logistic regressionRepresentativeness heuristicDescriptive statisticsInteractive voice responseTelephone interviewMedicineObservational studyResearch designPsychologyFamily medicineSocial psychologyComputer scienceStatisticsGeography

Abstract

fetched live from OpenAlex

BACKGROUND: Sequential mixed-mode surveys using both web-based surveys and telephone interviews are increasingly being used in observational studies and have been shown to have many benefits; however, the application of this survey design has not been evaluated in the context of epidemiological case-control studies. OBJECTIVE: In this paper, we discuss the challenges, benefits, and limitations of using a sequential mixed-mode survey design for a case-control study assessing risk factors during the COVID-19 pandemic. METHODS: Colorado adults testing positive for SARS-CoV-2 were randomly selected and matched to those with a negative SARS-CoV-2 test result from March to April 2021. Participants were first contacted by SMS text message to complete a self-administered web-based survey asking about community exposures and behaviors. Those who did not respond were contacted for a telephone interview. We evaluated the representativeness of survey participants to sample populations and compared sociodemographic characteristics, participant responses, and time and resource requirements by survey mode using descriptive statistics and logistic regression models. RESULTS: Of enrolled case and control participants, most were interviewed by telephone (308/537, 57.4% and 342/648, 52.8%, respectively), with overall enrollment more than doubling after interviewers called nonresponders. Participants identifying as female or White non-Hispanic, residing in urban areas, and not working outside the home were more likely to complete the web-based survey. Telephone participants were more likely than web-based participants to be aged 18-39 years or 60 years and older and reside in areas with lower levels of education, more linguistic isolation, lower income, and more people of color. While there were statistically significant sociodemographic differences noted between web-based and telephone case and control participants and their respective sample pools, participants were more similar to sample pools when web-based and telephone responses were combined. Web-based participants were less likely to report close contact with an individual with COVID-19 (odds ratio [OR] 0.70, 95% CI 0.53-0.94) but more likely to report community exposures, including visiting a grocery store or retail shop (OR 1.55, 95% CI 1.13-2.12), restaurant or cafe or coffee shop (OR 1.52, 95% CI 1.20-1.92), attending a gathering (OR 1.69, 95% CI 1.34-2.15), or sport or sporting event (OR 1.05, 95% CI 1.05-1.88). The web-based survey required an average of 0.03 (SD 0) person-hours per enrolled participant and US $920 in resources, whereas the telephone interview required an average of 5.11 person-hours per enrolled participant and US $70,000 in interviewer wages. CONCLUSIONS: While we still encountered control recruitment challenges noted in other observational studies, the sequential mixed-mode design was an efficient method for recruiting a more representative group of participants for a case-control study with limited impact on data quality and should be considered during public health emergencies when timely and accurate exposure information is needed to inform control measures.

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)0.206
metaresearch head score (Gemma)0.042
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.554
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.2060.042
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.874
GPT teacher head0.664
Teacher spread0.210 · 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 designQualitative
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

Citations5
Published2024
Admission routes1
Has abstractyes

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