Artistic expression as a source of resilience for transgender and gender diverse young people
Bibliographic record
Abstract
There is a paucity of research exploring sources of resilience among transgender and gender diverse (TGD) young people with multiple marginalized identities. Information and communication technologies (ICTs) offer unique opportunities for authentic self-expression, which is not always possible offline. The primary aims of this study were to understand unique sources of resilience among TGD youth in their online and offline lives. Using photo elicitation and grounded theory methods, we conducted online in-depth interviews with TGD young people (N = 29) between the ages of 14–25 across the United States identifying with at least one of the following social statuses: (a) person of color, (b) immigrant, or (c) living in a rural area. Four themes were identified from the data, with both online and offline artistic expression being viewed as a: (1) form of authentic self-expression; (2) coping mechanism; (3) way to connect to others; and (4) pathway toward agency. Findings advance understanding about the use of artistic expression as an underexamined source of resilience among TGD youth with multiple marginalized identities. Within clinical settings, options for TGD youth to participate in various forms of expressive art may improve engagement and enhance youths’ abilities to authentically express their thoughts, feelings, and experiences to promote healing and growth.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".