Recruitment of Patients With Cancer for a Clinical Trial Evaluating a Web-Based Psycho-Oncological Intervention: Secondary Analysis of a Diversified Recruitment Strategy in a Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: Participant recruitment poses challenges in psycho-oncological intervention research, such as psycho-oncological web-based intervention studies. Strict consecutive recruitment in clinical settings provides important methodological benefits but is often associated with low response rates and reduced practicability and ecological validity. In addition to preexisting recruitment barriers, the protective measures owing to the COVID-19 pandemic restricted recruitment activities in the clinical setting since March 2020. OBJECTIVE: This study aims to outline the recruitment strategy for a randomized controlled trial evaluating the unguided emotion-based psycho-oncological online self-help (epos), which combined traditional and web-based recruitment. METHODS: We developed a combined recruitment strategy including traditional (eg, recruitment in clinics, medical practices, cancer counseling centers, and newspapers) and web-based recruitment (Instagram, Facebook, and web pages). Recruitment was conducted between May 2020 and September 2021. Eligible participants for this study were adult patients with any type of cancer who were currently receiving treatment or in posttreatment care. They were also required to have a good command of the German language and access to a device suitable for web-based interventions, such as a laptop or computer. RESULTS: We analyzed data from 304 participants who were enrolled in a 17-month recruitment period using various recruitment strategies. Web-based and traditional recruitment strategies led to comparable numbers of participants (151/304, 49.7% vs 153/304, 50.3%). However, web-based recruitment required much less effort. Regardless of the recruitment strategy, the total sample did not accurately represent patients with cancer currently undergoing treatment for major types of cancer in terms of various sociodemographic characteristics, including but not limited to sex and age. However, among the web-recruited study participants, the proportion of female participants was even higher (P<.001), the mean age was lower (P=.005), private internet use was higher (on weekdays: P=.007; on weekends: P=.02), and the number of those who were currently under treatment was higher (P=.048). Other demographic and medical characteristics revealed no significant differences between the groups. The majority of participants registered as self-referred (236/296, 79.7%) instead of having followed the recommendation of or study invitation from a health care professional. CONCLUSIONS: The combined recruitment strategy helped overcome general and COVID-19-specific recruitment barriers and provided the targeted participant number. Social media recruitment was the most efficient individual recruitment strategy for participant enrollment. Differences in some demographic and medical characteristics emerged, which should be considered in future analyses. Implications and recommendations for social media recruitment based on personal experiences are presented. TRIAL REGISTRATION: German Clinical Trials Register DRKS00021144; https://drks.de/search/en/trial/DRKS00021144. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1016/j.invent.2021.100410.
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 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 | 0,010 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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; un appel candidat d’une seule tête enseignante, 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 ».