Advancing the Science of Recruitment for Family Caregivers: Focus Group and Delphi Methods
Bibliographic record
Abstract
BACKGROUND: Successful recruitment of participants is imperative to a rigorous study, and recruitment challenges are not new to researchers. Many researchers have used social media successfully to recruit study participants. However, challenges remain for effective online social media recruitment for some populations. OBJECTIVE: Using a multistep approach that included a focus group and Delphi method, researchers performed this study to gain expert advice regarding material development for social media recruitment and to test the recruitment material with the target population. METHODS: In the first phase, we conducted a focus group with 5 social media experts to identify critical elements for effective social media recruitment material. Utilizing the Delphi method with 5 family caregivers, we conducted the second phase to reach consensus regarding effective recruitment videos. RESULTS: Phase I utilized a focus group that resulted in identification of three barriers related to social media recruitment, including lack of staff and resources, issues with restrictive algorithms, and not standing out in the crowd. Phase II used the Delphi method. At the completion of Delphi Round 1, 5 Delphi participants received a summary of the analysis for feedback and agreement with our summary. Using data and recommendations from Round 1, researchers created two new recruitment videos with additions to improve trustworthiness and transparency, such as the university's logo. In Round 2 of the Delphi method, consensus regarding the quality and trustworthiness of the recruitment videos reached 100%. CONCLUSIONS: One of the primary challenges for family caregiver research is recruitment. Despite the broad adoption of social media marketing approaches, the effectiveness of online recruitment strategies needs further investigation.
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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 | 0.346 | 0.257 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".