What We Know and Do Not Know About Camouflaging, Impression Management, and Mental Health and Wellbeing in Autistic People
Bibliographic record
Abstract
Camouflaging is an impression management strategy employed by some autistic people, widely seen as a response to the pervasive stigma surrounding autism in society. Autistic narratives and lived experiences consistently link camouflaging to anxiety, depression, suicide risks, and autistic burnout. Quantitative research is yet to determine the nature of these relationships, with a significant portion of recent studies providing inconsistent evidence. While camouflaging can be a compelled survival strategy in social environments, it might also contribute to positive outcomes such as securing employment and forming positive social relationships, implicating a complex interrelationship with mental health and wellbeing. We advocate for using a transactional impression management framework to understand camouflaging and wellbeing and address the inconsistencies in research. Through examining the transactions among a person's individual and cognitive characteristics, behavior modification strategies, and the particular social contexts they find themselves in, this framework guides new empirical research directions to delineate the relationships between camouflaging, impression management, mental health, and wellbeing. There is a need to develop multiple measures of camouflaging that delineate the motivations, ability, effortfulness, and perceived effectiveness of camouflaging and examine how a person's social behaviors are perceived in different social environments. Research should also focus on intersectionality, sociocultural influences, and diverse autistic voices to study context-sensitive camouflaging experiences across the autistic population.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".