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Record W4206622691 · doi:10.2196/preprints.29377

Strategies for improving recruitment of pregnant women to clinical research: An evaluation of social media versus traditional offline methods in Vancouver, Canada (Preprint)

2021· preprint· en· W4206622691 on OpenAlexaboutno aff
Kelsey M Cochrane, Jennifer A. Hutcheon, Crystal D Karakochuk

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicSocial Media in Health Education
Canadian institutionsnot available
Fundersnot available
KeywordsSocial mediaDemographyMedicineDemographicsPsychologyPolitical scienceSociology

Abstract

fetched live from OpenAlex

BACKGROUND Social media is an effective alternative to offline methods for participant recruitment to research. However, the effectiveness of social media compared with offline strategies among pregnant women is unclear. Further, it is unclear whether recruitment strategy alters demographic characteristics of participants. OBJECTIVE We aimed to estimate recruitment rates from social media and offline methods and to explore the whether participant demographics differed according to recruitment strategy in a clinical nutrition trial that recruited 60 healthy pregnant women in Vancouver, Canada. METHODS Facebook was used to run 9 social media campaigns, 10-18 days each (15-weeks total) and costing $50-$100 CAD ($675 CAD total). Offline methods were used concurrently over 64-weeks. A total of $300 CAD was spent on printing. Demographic characteristics of those recruited via each method was compared using bivariate statistics. Cost, rate of recruitment and conversion rate in each group was calculated. Performance metrics of social media campaigns, including reach, impressions, clicks, inquiries, and enrollments, were recorded. Linear regression was used to explore the association between metrics and dollars spent per campaign. RESULTS In total, n=481 inquiries were received (n=51 [11%] via offline methods; n=430 [89%] via social media). Enrollees (n=60) included n=24 (40%) and n=36 (60%) via offline and social media methods, respectively. Gestational weeks was provided by n=251 women (52%) upon inquiry (mean ± SD gestational weeks was 13.3 ± 4.7 and 13.2 ± 5.6 in the offline and social media groups, respectively, P=.96). There were no statistically significant differences in age (33 ± 3.2 and 33 ± 3.6, P=.67), ethnicity (58% and 56% Caucasian, P=.97), education (88% and 78% had University-level education, P=.64), household income (58% and 47% >$100,000 CAD/year, P=.26), pre-pregnancy BMI (22.2 ± 2.6 and 23.4 ± 2.8, P=.11), or parity (75% and 72% nulliparous, P=.81); results are presented for offline and social media, respectively. Direct cost/enrollee was $13 and $19 in those who were recruited via offline and social media methods, respectively (however, this does not include cost of labour). Rate of recruitment was ~6x faster via social media than offline methods, however, the conversion rate was higher via offline methods than social media (47% versus 8%). Overall, campaign metrics (reach, impressions, clicks, and inquiries) improved over time. Amount spent per campaign (controlling for campaign duration) was significantly associated with improved clicks (P=.01), and inquiries (P=.04), but not enrollments (P=.19). CONCLUSIONS Social media was more efficient and effective for recruitment of pregnant women than offline methods. We gained numerous insights for optimization of social media campaigns (dollars spent, attribution setting, photo testing, automatic optimization) to increase clicks and inquiries, however this does not necessarily increase enrollments, which was more dependent on study specific factors (e.g. time of year, study design, and intervention). CLINICALTRIAL ClinicalTrials.gov (identifier: NCT04022135). Registered on July-14-2019. https://clinicaltrials.gov/ct2/show/NCT04022135

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)0.035
metaresearch head score (Gemma)0.053
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.965
Threshold uncertainty score0.715

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0350.053
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0030.001
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.851
GPT teacher head0.648
Teacher spread0.202 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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