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Record W4223928480 · doi:10.1542/peds.2020-049437n

First Do No Harm: Suggestions Regarding Respectful Autism Language

2022· article· en· W4223928480 on OpenAlexaff
Patrick Dwyer, Jackie Ryan, Zachary J. Williams, Dena Gassner

Bibliographic record

VenuePEDIATRICS · 2022
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsUniversity of Alberta
FundersNational Institute of General Medical SciencesNational Institute on Deafness and Other Communication Disorders
KeywordsMedicineHarmAutismDo no harmPsychiatryLaw

Abstract

fetched live from OpenAlex

Nationally and internationally, efforts are ongoing to promote diversity, equity, and inclusion in healthcare and other fields. These efforts require consideration of ways in which language and assumptions impact individuals and communities. The autism and disability spheres are no exception. Indeed, the mental health of autistic people is predicted by the degree to which they feel society accepts them as autistic.1 Thus, we believe discourse that disparages autism could be harmful to autistic people’s well-being. Autistic individuals who face further stigma and discrimination due to other intersectional identities might be particularly vulnerable. Unfortunately, autism research and practice have traditionally used disparaging language grounded in the medical model.Some might object that alternatives to traditional medical model terms are subjective or unscientific. However, we believe traditional terminology is heavily laden with subjective value judgements. For example, the traditional term “disorder” has a decidedly negative connotation. It also implies that individuals’ own characteristics are responsible for their challenges, and it suggests a need to eliminate this disorder. In contrast, the more nuanced word “disability” allows both individual characteristics and societal or contextual barriers to contribute to challenges. The term disability thus appears to be both more scientifically appropriate and less stigmatizing toward a vulnerable population than disorder.In Table 1, we list various traditional terms and concepts that we believe are problematic, along with suggested replacements. We also suggest that practitioners and researchers balance a focus on autistic individuals' challenges with discussion of their strengths and potential. This balanced approach may be especially important for families of young children whose futures may be unclear and a source of considerable anxiety to caregivers.Furthermore, researchers and practitioners should be aware of an ongoing debate between supporters of identity-first (“autistic person”) and person-first (“person with autism”) language. Many autistic individuals support identity-first language2,3 and some fear that person-first language reflects negative attitudes toward autism.4 However, others endorse person-first language.2,3 The term “person on the autism spectrum” is often the most preferred term among autistic individuals and other stakeholder groups,2,3 and this verbiage is typically found to be acceptable by proponents of both person-first and identity-first language. Practitioners should ask about and respect the language preferences of individuals “on the spectrum” who can articulate their views.Overall, in light of concerns that typically-developing people struggle to understand autistic perspectives,5 we urge practitioners and researchers to strive to have empathy for how their language sounds to autistic people. We also suggest it can often be helpful to ask oneself if one would use similar phrasing with other marginalized communities. We feel that there needs to be a shift toward “cultural humility” and willingness to learn from autistic people about autistic identities and how to promote autistic well-being.Practitioners and researchers interested in a more detailed discussion of appropriate autism terminology should refer to Bottema-Beutel and colleagues.6 We provide definitions of neurodiversity terminology (eg, neurodiverse, neurodivergent) in Supplemental Table 2.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.633
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.026
GPT teacher head0.298
Teacher spread0.272 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations83
Published2022
Admission routes1
Has abstractyes

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