Adapting Peer Researcher Facilitated Strategies to Recruit People Receiving Mental Health Services to a Tobacco Treatment Trial
Notice bibliographique
Résumé
Introduction: One of the most challenging aspects of conducting intervention trials among people who experience severe mental illness (SMI) and who smoke tobacco, is recruitment. In our parent "QuitLink" randomized controlled trial (RCT), slower than expected peer researcher facilitated recruitment, along with the impact of COVID-19 pandemic restrictions, necessitated an adaptive recruitment response. The objectives of the present study were to: (i) describe adaptive peer researcher facilitated recruitment strategies; (ii) explore the effectiveness of these strategies; (iii) investigate whether recruitment strategies reached different subgroups of participants; and (iv) examine the costs and resources required for implementing these strategies. Finally, we offer experience-based lessons in a Peer Researcher Commentary. Methods: People were included in the RCT if they smoked at least 10 cigarettes a day and were accessing mental health support from the project's two partnering mental health organizations in Victoria, Australia. The majority of people accessing these services will have been diagnosed with SMI. Recruitment occurred over 2 years. We began with peer facilitated recruitment strategies delivered face-to-face, then replaced this with direct mail postcards followed by telephone contact. In the final 4 months of the study, we began online recruitment, broadening it to people who smoked and were accessing support or treatment (including from general practitioners) for mental health and/or alcohol or other drug problems, anywhere in the state of Victoria. Differences between recruitment strategies on key participant variables were assessed. We calculated the average cost per enrolee of the different recruitment approaches. Results: = 29, <0.001), but the samples were otherwise similar. After the initial investment in training and equipping peer researchers, the average cost of recruitment was AU$1,182 per participant-~US$850. Face-to-face recruitment was the most expensive approach and postcard recruitment the least (AU$1,648 and AU$928 per participant). Discussion: Peer researcher facilitated recruitment into a tobacco treatment trial was difficult and expensive. Widely dispersed services and COVID-19 restrictions necessitated non-face-to-face recruitment strategies, such as direct mail postcards, which improved recruitment and may be worthy of further research. Clinical Trial Registration: The trial is registered with ANZCTR (www.anzctr.org.au): ACTRN12619000244101 prior to the accrual of the first participant and updated regularly as per registry guidelines. The trial sponsor was the University of Newcastle, NSW, Australia.
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 | 0,416 | 0,488 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,005 | 0,006 |
| Science ouverte | 0,007 | 0,008 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,016 | 0,004 |
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 ».