Conducting Health Literacy Research With Hard-to-Reach Regional Culturally and Linguistically Diverse Populations: Evaluation Study of Recruitment and Retention Methods Before and During COVID-19
Notice bibliographique
Résumé
BACKGROUND: In health research, culturally and linguistically diverse (CALD) health care consumers are cited as hidden or hard to reach. This paper evaluates the approach used by researchers to attract and retain hard-to-reach CALD research participants for a study investigating health communication barriers between CALD health care users and health care professionals in regional Australia. As the study was taking place during the COVID-19 pandemic, subsequent restrictions emerged. Thus, recruitment and retention methods were adapted. This evaluation considered the effectiveness of recruitment and retention used throughout the pre-COVID and during-COVID periods. OBJECTIVE: This evaluation sought to determine the effectiveness of recruitment and retention efforts of researchers during a study that targeted regional hard-to-reach CALD participants. METHODS: Recruitment and retention methods were categorized into the following 5 phases: recruitment, preintervention data collection, intervention, postintervention data collection, and interviews. To compare the methods used by researchers, recruitment and retention rates were divided into pre-COVID and during-COVID periods. Thereafter, in-depth reflections of the methods employed within this study were made. RESULTS: This paper provides results relating to participant recruitment and retainment over the course of 5 research phases that occurred before and during COVID. During the pre-COVID recruitment phase, 22 participants were recruited. Of these participants, 15 (68%) transitioned to the next phase and completed the initial data collection phase. By contrast, 18 participants completed the during-COVID recruitment phase, with 13 (72%) continuing to the next phase. The success rate of the intervention phase in the pre-COVID period was 93% (14/15), compared with 84.6% (11/13) in the during-COVID period. Lastly, 93% (13/14) of participants completed the postintervention data collection in the pre-COVID period, compared with 91% (10/11) in the during-COVID period. In total, 40 participants took part in the initial data collection phase, with 23 (58%) completing the 5 research phases. Owing to the small sample size, it was not determined if there was any statistical significance between the groups (pre- and during-COVID periods). CONCLUSIONS: The success of this program in recruiting and maintaining regional hard-to-reach CALD populations was preserved over the pre- and during-COVID periods. The pandemic required researchers to adjust study methods, thereby inadvertently contributing to the recruitment and retention success of the project. The maintenance of participants during this period was due to flexibility offered by researchers through adaptive methods, such as the use of cultural gatekeepers, increased visibility of CALD researchers, and use of digital platforms. The major findings of this evaluation are 2-fold. First, increased diversity in the research sample required a high level of flexibility from researchers, meaning that such projects may be more resource intensive. Second, community organizations presented a valuable opportunity to connect with potential hard-to-reach research participants.
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 | 0,214 | 0,213 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,002 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».