Risk Identification in Perinatal Health Care Settings via Technology-Based Recruitment Methods: Comparative Study
Notice bibliographique
Résumé
Background Digital screening and intervention tools have shown promise in the identification and reduction of substance use in health care settings. However, research in this area is impeded by challenges in integrating recruitment efforts into ongoing clinical workflows or staffing multiple study clinics with full-time research assistants, as well as by the underreporting of substance use. Objective The aim of the study is to evaluate pragmatic methods for facilitating study recruitment in health care settings by examining recruitment rates and participant characteristics using in-person–based versus flyer approaches. Methods This study compared recruitment rates at a Women’s Health clinic in the Midwest under 2 different recruitment strategies: in person versus via a flyer with a QR code. We also examined the disclosure of substance use and risk screener positivity for the 2 strategies. We also obtained information about the current use of technology and willingness to use it for study participation. Results A greater percentage of patients recruited in person participated than those recruited via flyers (57/63, 91% vs 64/377, 17%). However, the final number recruited in each group was roughly equal (n=57 vs n=64). Additionally, participants recruited via flyers were more likely to screen positive for alcohol use risk on the Tolerance, Annoyed, Cut Down, Eye-Opener alcohol screen than those recruited at the clinic (24/64, 38% vs 11/57, 19%; χ21=4.9; P=.03). Participants recruited via flyers were also more likely to screen positive for drug use risk on the Wayne Indirect Drug Use Screener than those recruited at the clinic (20/64, 31% vs 9/57, 16%; χ21=4.0; P=.05). Furthermore, of the 121 pregnant women, 117 (96.7%) reported owning a smartphone, 111 (91.7%) had an SMS text message plan on their phone, and 94 (77.7%) reported being willing to receive SMS text messages or participate in a study if sent a link to their phone. Conclusions The distribution of flyers with a QR code by medical staff appears to be an efficient and cost-effective method of recruitment that also facilitates disclosure while reducing the impact on clinic workflows. This method of recruitment can be useful for data collection at multiple locations and lead to larger samples across and between health systems. Participant recruitment via technology in perinatal health care appears to facilitate disclosure, particularly when participants can learn about the research and complete screening using their own device at a place and time convenient for them. Pregnant women in an urban Midwestern hospital had access to and were comfortable using technology.
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 | 0,025 | 0,048 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 ».