Effectiveness and Cost of Using Facebook Recruitment to Elicit Canadian Women’s Perspectives on Bone Health and Osteoporosis: Cross-Sectional Survey Study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.044 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".