Considerations in Recruiting Caregivers of Older Adults to Qualitative Internet-Mediated Research Using Facebook and Meta Business Suite
Bibliographic record
Abstract
BACKGROUND: Internet-mediated research (IMR), increasingly prominent in social sciences and health care, uses online platforms for data gathering, offering cost-effectiveness and wide accessibility. Despite assumptions that older adults are less active on social media, recent trends indicate otherwise, with a notable presence on platforms like Facebook, making it a valuable recruitment tool. METHODS: The Saskatchewan Caregiver Experience Study employed purposive maximum variation sampling to recruit caregivers via paid Facebook ads, manually shared Facebook posts, and community newsletters. Metrics such as reach, impressions, and link clicks from Facebook advertisements were used to evaluate recruitment effectiveness. Data quality was ensured through "one response per IP address" restrictions on SurveyMonkey. RESULTS: We recruited 355 survey respondents who met the study inclusion criteria. Participants had a mean age of 60.9 years (range: 22-87). Paid Facebook ads were the most effective recruitment method, indicated by higher engagement and response rates. The 355 survey responses totaled 40 746 words, reflecting strong participant engagement. The absence of financial incentives in the study also likely improved data quality. DISCUSSION: This method requires participants to have both device access and technological literacy. The study demonstrates the effectiveness of using social media for recruiting in qualitative research, highlighting its potential for inclusivity and representativeness, while also underscoring the importance of ethical considerations in IMR.
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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 | 0.621 | 0.574 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.012 | 0.017 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.008 | 0.013 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".