Intentional use and self-efficacy as distinct facets of impression management and their relationships with socio-motivational, cognitive, and mental health factors
Bibliographic record
Abstract
Human beings regularly adjust behaviors across social contexts as part of impression management (IM). Recently, "camouflaging" has been described as the behavioral strategies autistic individuals employ to blend into neurotypical social norms, often at costs to psychological wellbeing. It remains unclear whether camouflaging is unique to autism or overlaps with established IM constructs in terms of shared latent facets, socio-motivational and cognitive drivers, and mental health outcomes. To address this knowledge gap, we surveyed a representative US general population sample of 972 adults, utilizing self-report measures to assess camouflaging/IM, along with their theoretical socio-motivational and cognitive antecedents and mental health consequences. We first applied joint exploratory factor analysis to identify the latent facets underlying measures across camouflaging and existing IM constructs. Two latent IM facets emerged: "intentional use" (purposeful IM use) and "self-efficacy" (self-perceived IM capacity). Structural equation modeling suggested that greater IM intentional use was driven by socio-motivational pressures and predicted poorer mental health, whereas stronger IM self-efficacy was supported by executive functioning and perspective-taking and linked to better mental health. Neurodivergent traits exhibited unique moderation effects; in those with elevated autistic traits, greater IM intentional use and self-efficacy were both linked to poorer mental health. Yet, in those with elevated ADHD traits, greater IM self-efficacy was linked to better mental health. Critically, greater IM self-efficacy may buffer the negative impacts of IM intentional use on mental health. Our findings reveal an expanded understanding of camouflaging as part of multi-faceted IM, which exhibits complex relationships with mental health, moderated by neurodivergence. The implications point to conceptual and methodological advances for social coping research across neurodiverse groups, especially for developing tailored support.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".