Cross-cultural variation in experiences of acceptance, camouflaging and mental health difficulties in autism: A registered report
Bibliographic record
Abstract
Recent findings suggest that stigma and camouflaging contribute to mental health difficulties for autistic individuals, however, this evidence is largely based on UK samples. While studies have shown cross-cultural differences in levels of autism-related stigma, it is unclear whether camouflaging and mental health difficulties vary across cultures. Hence, the current study had two aims: (1) to determine whether significant relationships between autism acceptance, camouflaging, and mental health difficulties replicate in a cross-cultural sample of autistic adults, and (2) to compare these variables across cultures. To fulfil these aims, 306 autistic adults from eight countries (Australia, Belgium, Canada, Japan, New Zealand, South Africa, the United Kingdom, and the United States) completed a series of online questionnaires. We found that external acceptance and personal acceptance were associated with lower levels of depression but not camouflaging or stress. Higher camouflaging was associated with elevated levels of depression, anxiety, and stress. Significant differences were found across countries in external acceptance, personal acceptance, depression, anxiety, and stress, even after controlling for relevant covariates. Levels of camouflaging also differed across countries however this effect became non-significant after controlling for the covariates. These findings have significant implications, identifying priority regions for anti-stigma interventions, and highlighting countries where greater support for mental health difficulties is needed.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".