Camouflaging, internalized stigma, and mental health in the general population
Bibliographic record
Abstract
BACKGROUND: Camouflaging, the strategies that some autistic people use to hide their differences, has been hypothesized to trigger mental health ramifications. Camouflaging might reflect ubiquitous impression management experiences that are not unique to autistic people and similarly impact the mental health of non-autistic people. AIMS: We first examined whether individuals in the general population camouflage and manage impressions while experiencing mental health repercussions, and how gender and neurodivergent traits modified these associations. We then assessed how camouflaging and impression management arose from internalized stigma, and their inter-relationships in shaping mental health outcomes. METHODS: Data were collected from 972 adults from a representative U.S. general population sample, with measures pertaining to camouflaging, impression management, mental health, internalized stigma, and neurodivergent traits. Multivariate hierarchical regression and moderated mediation analyses were used to address the two research aims. RESULTS: Both camouflaging and self-presentation (a key component of impression management) were associated with mental health presentations in the general population, which overlapped with those previously reported in autistic people. These associations were more pronounced in women compared with men and were of different directions for individuals with higher autistic traits versus higher ADHD traits. Internalized stigma might be a key stressor that could elicit camouflaging and impression management through social anxiety, which in turn might lead to adverse mental health outcomes. CONCLUSIONS: These findings advance the conceptual clarity and clinical relevance of camouflaging and impression management across social and neurodiverse groups in the general population. The ramifications of camouflaging and impression management underscore the need to alleviate internalized stigma for better mental health across human groups.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".