Strategies for improving recruitment of pregnant women to clinical research: An evaluation of social media versus traditional offline methods in Vancouver, Canada (Preprint)
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,035 | 0,053 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».