Digital microaggressions and LGBTQ+ youth: exploring potential impacts and opportunities for educational intervention
Bibliographic record
Abstract
LGBTQ+ youth frequently leverage the affordances of internet-enabled information and communication technologies (ICTs) to support their identity development, mental health, and well-being. Yet, anti-LGBTQ+ intolerance simultaneously persists in their digitally mediated contexts—including in the form of digital microaggressions. Data from an online survey of LGBTQ+ youth (age 14–24) residing across the United States, United Kingdom, and Canada were used to explore the relationships between six types of digital microaggressions, perceived stress, mental health, and psychological well-being through structural equation modelling. Findings suggest that exposure to some types of digital microaggressions may produce direct, incremental, and negative impacts on LGBTQ+ youth. Experiencing and witnessing discriminatory digital microaggressions had the most consistently significant relationships. Opportunities for future scholarship and insights for supportive intervention by caregivers, educators, and other professionals are discussed.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".