“Internet hugs”: The use of Reddit as a valuable form of social support during medical transitioning
Bibliographic record
Abstract
Background Transgender and gender diverse (TGD) individuals experience significant health disparities, and are medically disadvantaged, experiencing various barriers to accessing and receiving healthcare. Given persistent anti-transgender stigma and discrimination, TGD individuals may use online resources and social media (e.g. Reddit), for social support, including to access information about medical transitioning. However, TGD individuals’ use of Reddit for social support has not yet been explored, especially within the context of Ontario, Canada’s largest province.Methods To explore various forms of social support that TGD individuals access through social media, we webscraped and performed thematic analysis on two subreddits specific to TGD individuals residing in Ontario.Results Our dataset included 3912 posts and 23162 comments. Users engaged in four types of social support. Appraisal support was reflected in the theme of mutual understanding through shared identity and challenges. Emotional support was exhibited through the theme of creating compassionate connections despite anonymity. Instrumental support was characterized by the theme of tangible help to alleviate stress. Informational support was the most commonly observed and included three distinct themes: seeking medical information and advice, providing support through personal experience, and sharing legislative or medical information.Conclusions Our findings highlight that Reddit appears to be a helpful resource for navigating medical transitioning and accessing support and information that is beneficial for accessing and receiving healthcare among TGD individuals in Ontario.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".