It’s my safe space: The life-saving role of the internet in the lives of transgender and gender diverse youth
Bibliographic record
Abstract
Background: Public awareness of Transgender and Gender Diverse (TGD) identities has grown significantly; however, acceptance and support remain elusive for many TGD youth. Resultant experiences of marginalization and stigmatization contribute to elevated rates of psychological distress and suicidality among TGD youth. Emergent evidence suggests that the internet may offer TGD youth safety, support, and community previously unavailable.Aim: The primary aim of this qualitative inquiry is to engage in an in-depth exploration of the online experiences and processes which help protect against psychological distress and promote well-being among TGD youth.Methods: Data were culled from a mixed-methods, online study of sexual and gender minority youth from across the United States and Canada which followed Institutional Review Board approved protocols. Participants for this study represent a sample (n = 260) of TGD participants aged 14–22 (x̄ = 17.30). Data were analyzed using Charmaz’ grounded theory strategies.Results: Data revealed that the internet offers TGD youth affirming spaces that, for the most part, do not exist in their offline lives. Online, TGD youth were able to engage meaningfully with others as their authentic selves, often for the first time. These experiences fostered well-being, healing, and growth through five processes: 1. Finding an escape from stigma and violence, 2. Experiencing belonging, 3. Building confidence, 4. Feeling hope, and 5. Giving back.Discussion: The unique and innovative ways in which participants use online spaces to foster resilience offer important insights to inform affirmative practices with TGD young people.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".