Tracing participants for longitudinal environmental health research using social networking sites: a pilot study
Bibliographic record
Abstract
Aim: Longitudinal cohort study designs are considered the gold standard for investigating associations between environmental exposures and human health yet they are characterized by limitations including participant attrition, and the resource implications associated with cohort recruitment and follow-up. Attrition compromises the integrity of research by threatening both the internal and external validity of empirical results, weakening the accuracy of statistical inferences and the generalizability of findings. This pilot study aimed to trace participants from a historical cohort study, the Hamilton Child Cohort Study (HCC) (n = 3,202), (1976–1986, 2003–2008) which was originally designed to examine the relative contribution of indoor and outdoor exposure to air pollution on respiratory health. Methods: Original participants were traced through social networking sites (SNS) by leveraging personal identifying data (name, age, sex, educational affiliation, and geographical locations) from the HCC entered into SNS search engines. Results: Of the original cohort (n = 3,166), 21% (n = 665) were identified as having social media presence (SMP) on a single social media platform, with 15% (n = 479) found on Facebook, 6% (n = 185) on LinkedIn, < 1% (n = 9) on Instagram, and n = 1 participant on Twitter. However, 68% (n = 2,168) of the cohort were associated with multiple SNS with the same features (matching names, ages, and locations), making conclusive identification challenging. The remaining 11% (n = 334) of the cohort had no SMP (NSMP). Statistical differences in sample characteristics of each cohort were analyzed using the Pearson chi-square test. Significant differences between the SMP and NSMP cohorts were found in relation to sex (p < 0.001), and childhood neighborhood of residence (p < 0.05). Conclusions: This study underscores social media’s potential for tracing participants in longitudinal studies while advising a multi-faceted approach to overcome inherent limitations and biases. A full-scale study is necessary to determine whether utilizing SNS to trace participants for longitudinal research is an effective tool for re-engaging research participants lost to attrition.
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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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".