Effectiveness and Cost of Using Facebook Recruitment to Elicit Canadian Women’s Perspectives on Bone Health and Osteoporosis: Cross-Sectional Survey Study
Notice bibliographique
Résumé
BACKGROUND: Surveys can help health researchers better understand the public's perspectives and needs regarding prevalent conditions such as osteoporosis, which affects more than two-thirds of postmenopausal women. However, recruitment of large cohorts for survey research can be time-consuming and expensive. With 2.9 billion active users across the globe and reasonable advertising costs, Facebook (Meta Platforms, Inc) has emerged as an effective recruitment tool for surveys, although previous studies have targeted young populations (<50 years of age) and none have focused on bone health. OBJECTIVE: We assessed the effectiveness and cost of using Facebook to recruit Canadian women aged ≥45 years to share their perspectives on bone health and osteoporosis via a web-based survey. METHODS: We developed a 15-minute web-based survey with the goal of eliciting perspectives on bone health and osteoporosis. A Facebook advertisement was placed for 2 weeks in February 2022, during which time it was shown to women of age ≥45 years who resided in Canada, inviting them to participate and offering a chance to win 1 of 5 CAD $100 gift cards (at the time of this study [February 14, 2022], a currency exchange rate of CAD $1=US $0.79 was applicable). Those who clicked on the advertisement were taken to an eligibility screening question on the survey home screen. Individuals who confirmed eligibility were automatically directed to the first survey question. All individuals who answered the first survey question were considered participants and included in the analyses. We determined the survey reach, click rate, cooperation rate, completion rate, cost per click, and cost per participant. Sociodemographic characteristics of respondents were compared with data from the 2021 Canadian Census. RESULTS: The Facebook advertisement was shown to 34,086 unique Facebook users, resulting in 2033 link clicks (click rate: 6.0%). A total of 1320 individuals completed the eligibility screening question, 1195 started the survey itself (cooperation rate: 58.8%), and 966 completed the survey (completion rate: 47.5%). The cost of the advertising campaign was CAD $280.12, resulting in a cost per click of CAD $0.14 and a cost per participant of CAD $0.23. The 1195 participants ranged in age from 45-89 years (mean 65, SD 7 years), 921 (93.7%) were of White ethnicity, 854 (88.3%) had completed some postsecondary education, and 637 (65.8%) resided in urban areas. Responses were received from residents of all 10 Canadian provinces and 2 of 3 territories. When compared to 2021 Canadian Census data, postsecondary education and rural residence were overrepresented in our study population. CONCLUSIONS: Facebook advertising is an efficient, effective, and inexpensive way of recruiting large samples of older women for participation in web-based surveys for health research. However, it is important to recognize that this modality is a form of convenience sampling and the benefits of Facebook recruitment must be balanced with its limitations, which include selection bias and coverage error.
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 | 0,044 | 0,134 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,005 | 0,002 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».