Association of Social Media Recruitment and Depression Among Racially and Ethnically Diverse Metabolic and Bariatric Surgery Candidates: Prospective Cohort Study
Bibliographic record
Abstract
Background: Due to the widespread use of social media and the internet in today's connected world, obesity and depression rates are increasing concurrently on a global scale. This study investigated the complex dynamics involving social media recruitment for scientific research, race, ethnicity, and depression among metabolic and bariatric surgery (MBS) candidates. Objective: This study aimed to determine (1) the association between social media recruitment and depression among MBS candidates and (2) racial and ethnic differences in social media recruitment engagement. Methods: The analysis included data from 380 adult MBS candidates enrolled in a prospective cohort study from July 2019 to December 2022. Race and ethnicity, recruitment method (social media: yes or no), and depression status were evaluated using χ2 tests and logistic regression models. Age, sex, and ethnicity were adjusted in multivariable logistic regression models. Results: The mean age of the candidates was 47.35 (SD 11.6) years, ranging from 18 to 78 years. Participants recruited through social media (n=41, 38.32%) were more likely to report past or current episodes of depression compared to nonsocial media-recruited participants (n=74, 27.11%; P=.03), with a 67% increased likelihood of depression (odds ratio [OR] 1.67, 95% CI 1.04-2.68, P=.03). Further analysis showed that participants with a history of depression who were below the mean sample age were 2.26 times more likely to be recruited via social media (adjusted OR [aOR] 2.26, 95% CI 1.03-4.95; P=.04) compared to those above the mean age. Hispanic (n=26, 38.81%) and non-Hispanic White (n=53, 35.10%) participants were significantly more likely to be recruited via social media than non-Hispanic Black (n=27, 18.37%) participants (P<.001). After adjusting for covariates, non-Hispanic Black participants were 60% less likely than non-Hispanic White participants to be recruited via social media (aOR 0.40, 95% CI 0.22-0.71; P=.002). Conclusions: We found that individuals recruited through social media channels, especially younger participants, were more likely to report past or current episodes of depression compared to those recruited through nonsocial media. The study also showed that non-Hispanic Black individuals are less likely to engage in social media recruitment for scientific research versus other racial and ethnic groups. Future mental health-related studies should consider strategies to mitigate potential biases introduced by recruitment methods to ensure the validity and generalizability of research findings.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".