Improving Data Integrity in Samples Obtained From Web-Based Recruitment: Protocol for the Development of a Novel System for Assessing Participant Authenticity in a Remote Longitudinal Cohort Study of Polysubstance Use
Notice bibliographique
Résumé
BACKGROUND: Remote recruitment for human participant research is increasingly popular due to its speed, cost-effectiveness, and accessibility for participants. However, in some cases, it can be particularly difficult to authenticate participants recruited remotely, which, unless adequately addressed, may pose a threat to data integrity and validity. OBJECTIVE: This protocol aims to outline authenticity concerns encountered via remote recruitment for a longitudinal cohort study of adults reporting polysubstance use. Stemming from these concerns, we describe the development of a novel system of participant authenticity checks, designed with the goal of maximizing data integrity and minimizing the introduction of additional barriers to participating in the research. Finally, we examine rates of passing each active authenticity check among participants recruited via web-based advertisements. METHODS: Participants were recruited through one of several modalities, including via electronic health records and a third-party company managing a web-based advertising campaign. All participants enrolled in the longitudinal study completed a screening survey, followed by a baseline assessment (involving a survey and an interview) before completing up to 4 weekly interviews and follow-up assessments at 4, 8, and 12 months after baseline. The authenticity check system described here was implemented for all participants recruited via web-based advertising. In addition to passive authenticity checks (ie, randomized online survey passwords), we describe a five-step active authentication protocol: (1) reviewing interest forms for duplication (interest form duplication review), (2) an attention check at screening (attention check), (3) reviewing personal information after completion of the screening survey for duplicates or inconsistencies (personal information verification), (4) a verbal identity confirmation at baseline (verbal identity confirmation), and (5) a review of participant responses for inconsistent reporting at baseline (consistent reporting review). RESULTS: In total, 178 (6.85%) of the 2598 active authenticity checks administered were failed, leading to the exclusion of 119 unique potential participants due to fraudulent, inconsistent, or ineligible submissions. The 119 unique exclusions represented 11.13% (119/1069) of the potential participants identified via web-based advertising. Reviewing personal information provided at screening for inconsistencies (personal information verification) accounted for the largest number of failed checks (100/178, 56.2%), whereas reviewing interest form entries for duplicate personal information (interest form duplication review) yielded the fewest failures (7/178, 3.9%). CONCLUSIONS: The system presented provides an example of how researchers may increase confidence in the authenticity of participants recruited remotely, while avoiding the introduction of potential barriers to participating in research, such as requiring photo ID, online video call verification, or in-person verification. Such additional requirements for participants may systematically bias samples, especially when conducting research with populations that have been historically marginalized or those with stigmatized health conditions or behaviors. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/69956.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,121 | 0,038 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».