Associations Between Psychosocial Measures and Digital Media Use Among Transgender Youth: Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: Transgender, nonbinary, and gender-diverse (TNG) youth encounter barriers to psychosocial wellness and also describe exploring identities and communities on the web. Studies of cisgender youth connect increased digital technology use with lower well-being, parent relationships, and body image scores as well as increased loneliness and fear of missing out (FOMO). However, little is known about the psychosocial factors associated with digital technology use among TNG compared with cisgender youth. OBJECTIVE: This study aims to examine the associations between psychosocial measures and digital technology use and its importance for cisgender and TNG youth. METHODS: We surveyed a nationally representative sample of adolescents (aged 13-18 years) about psychosocial wellness and digital technology use. Psychosocial measures included assessment of well-being, parental relationships, body image, loneliness, and FOMO. Digital media use assessments included the short Problematic and Risky Internet Use Screening Scale-3 and the Adolescent Digital Technology Interactions and Importance (ADTI) scale and subscales. We compared psychosocial measures between gender identity groups. We also compared stratified correlations for psychosocial measures (well-being, parent relationships, body image, loneliness, and FOMO) with ADTI and Problematic and Risky Internet Use Screening Scale-3 scores between gender identity groups. All comparisons were adjusted for age, race, and ethnicity. RESULTS: Among 4575 adolescents, 53 (1.16%) self-identified as TNG youth. TNG youth had lower scores for well-being (23.76 vs 26.47; P<.001), parent relationships (19.29 vs 23.32; P<.001), and body image (13.50 vs 17.12; P<.001), and higher scores for loneliness (9.28 vs 6.55; P<.001) and FOMO (27.93 vs 23.89; P=.004), compared with cisgender peers. In a pattern different from that of their cisgender peers, better well-being scores and body image for TNG youth predicted higher problematic internet use (PIU) scores (correlation coefficients of 0.32 vs -0.07; P=.004 and 0.26 vs -0.21; P=.002, respectively). FOMO was a stronger positive predictor of higher ADTI total and subscale scores for cisgender youth compared with TNG youth. CONCLUSIONS: Overall, this study supports previously demonstrated disparities in the psychosocial wellness of TNG youth and adds that these disparities include loneliness and FOMO. This study shows prediction of PIU by both higher well-being and better body image, indicating that PIU may not be unilaterally driven by problematic factors among TNG youth. We suggest that this may be because of the specific digital media functions that TNG youth engage with as a disenfranchised population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".