Facebook: an effective tool for participant retention in longitudinal research
Bibliographic record
Abstract
BACKGROUND: Facebook is currently one of the world's most visited websites, and home to millions of users who access their accounts on a regular basis. Owing to the website's ease of accessibility and free service, demographic characteristics of users span all domains. As such, Facebook may be a valuable tool for locating and communicating with participants in longitudinal research studies. This article outlines the benefit gained in a longitudinal follow-up study, of an intervention programme for at-risk families, through the use of Facebook as a search engine. RESULTS: Using Facebook as a resource, we were able to locate 19 participants that were otherwise 'lost' to follow-up, decreasing attrition in our study by 16%. Additionally, analysis indicated that hard-to-reach participants located with Facebook differed significantly on measures of receptive language and self-esteem when compared to their easier-to-locate counterparts. CONCLUSIONS: These results suggest that Facebook is an effective means of improving participant retention in a longitudinal intervention study and may help improve study validity by reaching participants that contribute differing results.
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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 | 0.057 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".