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Record W4405004320 · doi:10.3389/fpsyt.2024.1513447

Editorial: Break the stigma: autism

2024· editorial· en· W4405004320 on OpenAlexaff
Nichole E. Scheerer, Catalina S. M. Ng, Ava N. Gurba, Morgan L. McNair, Matthew D. Lerner, April Hargreaves

Bibliographic record

VenueFrontiers in Psychiatry · 2024
Typeeditorial
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsAutismPsychologyPsychosocialStigma (botany)Psychological interventionDevelopmental psychologyAutonomyClinical psychologyPsychiatryPolitical science

Abstract

fetched live from OpenAlex

Stigma can be extremely impactful. For autistic people, stigma increases camouflaging behaviours aimed at concealing their autistic traits (4), it interferes with their development of self-determination and autonomy (5), it undermines their psychosocial well-being (1,3), and it leads to adverse consequences such as suicidality (i.e., suicidal ideation, self-harm and suicidal attempts (6)). Further, for parents of autistic children, stigma around bilingualism was shown to discourage families from raising their autistic child bilingually (7).Autism stigma has been also shown to significantly impede the integration of autistic individuals into society, a theme addressed by multiple studies in this special edition. Persistent barriers, inadequate support systems, and entrenched societal attitudes exacerbate this issue, as is often seen within education. For example, in South Korea, Yoon et al. (8) highlight how systemic stigmatization in secondary education leads to bullying, trauma, and exclusion from further education and employment. The study emphasizes how societal values like elitism and meritocracy worsen these challenges, underscoring the need for targeted interventions. This issue is not unique to Korea. Ahlers et al. (9) explored the isolation of autistic students in self-contained classrooms in the U.S. and revealed that educators' attitudes contribute to this exclusion. They advocate for more inclusive practices that extend beyond physical integration and call for strategies to enhance educators' understanding of autism. One such strategy, investigated by Jenks et al. (10) in the UK, is to run a training program for university staff aimed at debunking stereotypes and improving understanding of autism. Although quantitative measures showed limited changes, qualitative data revealed substantial benefits, particularly through including autistic perspectives. This approach enhanced staff's nuanced understanding and practical application in their interactions with autistic students.Beyond education, integration barriers also exist in social service provision. Li and Qi (11) examined the challenges faced by NGOs in China working with autistic children, noting how funding structures and interactions with funders impacted the effectiveness of inclusion efforts. This study provides valuable insights into the barriers in social service provision that hinder the integration of autistic children and suggests the need for more targeted and effective solutions.In broader society, Boucher et al. (12) explored how non-autistic adults quickly form negative judgments about autistic children based on brief interactions. They found that adults with higher social competence and explicit autism stigma were more likely to hold negative perceptions of autistic children. Similarly, Jones and Sasson (13) found that college undergraduates often displayed patronizing and exclusionary attitudes towards autism. These studies underscore the need to address biases that contribute to social exclusion.Collectively, these studies call for more focus on autistic strengths, inclusive practices, better-informed societal attitudes, and targeted interventions to support the integration of autistic individuals across various educational and social contexts. From initial diagnosis, clinicians should provide strength-based information to highlight autism strengths and reduce stigma (14). To provide more support to autistic people, Shaw et al. (6) suggest a neurodiversity-affirmative approach to autism which may promote a more positive self-identity and improved mental health. Similarly, Riebel et al. (15) highlight the role of promoting self-compassion in reducing the self-stigma and shame often associated with autism. Researchers also emphasized the need to provide more opportunities for autistic people to make choices and exert autonomy (5,16). For example, Glaves and Kolman (1) advise that clinicians should take an intersectional perspective of their autistic clients' gender identities to reduce stigma and have a better understanding of the needs of the whole person. Providing adequate support and better educating autistic medical professionals may promote inclusion in the medical workforce (6).Another salient theme in this special issue was the role of research(ers) in perpetuating autism stigma. As researchers, we need to explicitly address the link between ableism and poor autism science (17). This means shifting away research that reduces autistic people to their perceived deficits and instead focuses on how socially constructed views of "abilities" contribute to autistic people's "disabilities". This can be achieved by centering autistic voices (18)(19)(20). Comprehensive participatory research promotes close collaboration with the autistic community and other autism stakeholders across all stages of research, allowing autistic people to share their perspectives and shape research priorities (17)(18)(19). Researchers also need to leverage the unique contributions autistic researchers bring to autism research. For example, when qualitative interviews are conducted by autistic/non-autistic researcher dyads, autistic participants report increased connection and comfort (18). Approaches like these that centre the autistic voice facilitate closer alignment and trust between autism research(ers) and the autistic community, promote novel research programs that are relevant to the priorities of autistic people, create more ethical and less ableist research practices, and ultimately culminate in reduced autism stigma.Together the articles in this special issue stress the fact that researchers, clinicians, and society more broadly need to do a better job at advocated for the rights of autistic people. This starts with the understanding that autistic people are a marginalized population that experience discrimination (19). It is not autism itself that leads to a poor quality of life, but instead, a lack of social support and acceptance. Social interactions are bidirectional, yet autistic people are under enormous pressure to learn about and accommodate the needs and preferences of non-autistic people (21). We need to shift our focus away from the outdated notion that autistic people need to be fixed and instead place the onus on non-autistic people to learn about and accommodate the needs of autistic people (19). By teaching and promoting neurodiversity, or the understanding that there are no "right" kinds of brains, non-autistic people can learn to accept and value autistic differences (10,(21)(22). Virtual autism acceptance programs for children (22) and high schoolers (21), and a training program for higher education staff (10), all demonstrated success in reducing stigma, but stigma reduction can also be achieved through greater social inclusion. For example, service dog placements were found to act as a social catalyst, decreasing experiences of judgement and stigma for autistic children by inviting others to approach and interact (23). By increasing non-autistic people's acceptance of individuals whose behaviours may not align with society's expectations, autistic people may garner more social support and ultimately experience an improvement in their quality of life.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.032
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.020
Meta-epidemiology (narrow)0.0050.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0040.001
Science and technology studies0.0030.004
Scholarly communication0.0060.006
Open science0.0050.002
Research integrity0.0180.017
Insufficient payload (model declined to judge)0.0320.019

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.

Opus teacher head0.008
GPT teacher head0.283
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

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".

Quick stats

Citations4
Published2024
Admission routes1
Has abstractyes

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