Autistic people's perception of social camouflaging: Qualitative analysis of a web forum
Bibliographic record
Abstract
PurposeCamouflaging their autistic characteristics is a common coping strategy among autistic people, often leading to diagnostic delays and negative impacts on mental health. Although existing literature has examined autistic people’s views on camouflaging, it rarely explores these perspectives through natural conversations outside of research settings. To address this gap, we analyzed autistic people’s perception of camouflaging based on online discussions between autistic people.MethodologyThis study examined 194 posts from 68 users on a UK-based autism forum, employing inductive thematic analysis to identify key aspects of camouflaging.FindingsThe analysis revealed a predominantly negative view of camouflaging, with most users employing it primarily for social integration, especially in work settings. The most reported consequence was exhaustion. Not camouflaging leads to improved mental health, but also potentially to rejection.OriginalityOur findings strengthen existing knowledge about camouflaging by adding new analysis conducted directly on conversations happening organically. Future research should collect more data from natural conversations to validate these results and consider diverse sources of autistic individuals' perspectives.Keywords: social camouflaging, masking, autism spectrum disorder, web forum, perception
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".