Cultural Psychiatry Across (Cyber)Spaces: Understanding the Role of Fan Cultures in Mental Health
Bibliographic record
Abstract
Fan cultures emerge from communities of shared interests, such as those related to television series, novels, video games, and other hobbies. The internet has expanded the popularity and accessibility of fan cultures, with implications for mental health that remain underexplored within clinical practice and research. This narrative review and viewpoint article ponders how the recognition of fan cultures in cultural psychiatry may contribute to improving mental health care for members of fan communities. First, we provide an overview of the role of fan cultures in mental health, with a particular focus on furry culture, a well-studied but often stigmatized community. Drawing from media psychology and fan studies, we review the psychological and social affordances of fan cultures, and we take the furry culture as an example to further discuss how identity, technology, stigma, and community interface with fan culture and mental health. Then, we consider how the Cultural Formulation Interview, a conventional method of cultural psychiatry, could allow the mental health ramifications of fan cultures to be recognized in clinical 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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| 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".