Can Social Media Participation Enhance LGBTQ+ Youth Well-Being? Development of the Social Media Benefits Scale
Bibliographic record
Abstract
Social media sites offer critical opportunities for lesbian, gay, bisexual, trans, queer, and other sexual and/or gender minority (LGBTQ+) youth to enhance well-being through exploring their identities, accessing resources, and connecting with peers. Yet extant measures of youth social media use disproportionately focus on the detrimental impacts of online participation, such as overuse and cyberbullying. This study developed a Social Media Benefits Scale (SMBS) through an online survey with a diverse sample ( n = 6,178) of LGBTQ+ youth aged 14–29. Over three-quarters of the sample endorsed non-monosexual and/or and gender fluid identities (e.g., gender non-conforming, non-binary, pansexual, bisexual). Participants specified their five most used social media sites and then indicated whether they derived any of 17 beneficial items (e.g., feeling connected, gaining information) with the potential to enhance well-being from each site. An exploratory factor analysis determined the scale’s factor structure. Analysis of variance (ANOVA) and Sheffe post hoc tests examined age group differences. A four-factor solution emerged that measures participants’ use of social media for: (1) emotional support and development, (2) general educational purposes, (3) entertainment, and (4) acquiring LGBTQ+-specific information. Bartlett’s test of sphericity was significant (χ 2 = 40,828, p < .0005) and the scale had an alpha of .889. There were age group differences for all four factors ( F = 3.79–75.88, p < .05). Younger adolescents were generally more likely to use social media for beneficial factors than older youth. This article discusses the scale’s development, exploratory properties, and implications for research and professional practice.
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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 | 0.002 | 0.004 |
| 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 | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".