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Enregistrement W4390086591 · doi:10.1176/appi.pn.2024.01.1.13

APA’s Toolkit Encourages Reporters to Reject AAPI Stereotypes

2023· article· en· W4390086591 sur OpenAlexaboutno aff
Katie O’Connor

Notice bibliographique

RevuePsychiatric News · 2023
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueRacial and Ethnic Identity Research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésRacismChinese americansCoronavirus disease 2019 (COVID-19)Representation (politics)Media studiesGender studiesSociologyHistoryPsychologyCriminologyPolitical scienceEthnic groupLawPoliticsMedicine

Résumé

récupéré en direct d'OpenAlex

Back to table of contents Previous article Next article APA & MeetingsFull AccessAPA’s Toolkit Encourages Reporters to Reject AAPI StereotypesKatie O’ConnorKatie O’ConnorPublished Online:21 Dec 2023https://doi.org/10.1176/appi.pn.2024.01.1.13AbstractA new toolkit for reporters outlines the mental health ramifications of poor media representation of the Asian American/Pacific Islander (AAPI) community, as well as the discrimination and racism that result.As a fourth-generation San Franciscan, William Wong, M.D., grew up hearing firsthand about the systemic racism that three generations of his Chinese American family experienced. Their lives were deeply impacted by the Chinese Exclusion Act of 1882, which banned Chinese laborers from immigrating to the United States, and the Page Act of 1875, which prohibited Chinese women from entering the country.William Wong, M.D., saw discrimination toward the AAPI community intensify during the COVID-19 pandemic and reached out to APA to see what the psychiatric community could do to address it.Avery Wong PhotographyIn 2020, he saw a new wave of Asian American–directed racism sweep the country. Even before the COVID-19 pandemic lockdowns were implemented, Wong was confronted with discrimination when ordering ride shares. Then, after the lockdowns went into effect, drivers would slow down their cars and yell at him when he was out for walks. “This was in my own neighborhood,” said Wong, a psychiatrist with Kaiser Permanente in San Mateo, Calif. “It was quite shocking to me.”As Wong contemplated what could be done about these issues, so, too, did Seeba Anam, M.D., an associate professor of psychiatry and behavioral neuroscience at the University of Chicago. “I’m a big consumer of media, but I have never really found anything that reflects my experience or the experiences of my family or friends,” Anam said. “What I did find was discrimination modeled through stereotypes.” She saw the danger of this misrepresentation again during the early days of the COVID-19 pandemic, when stereotypes and discrimination in the media resulted in violence.In seeking to address these problems and raise public awareness about their mental health ramifications, Wong and Anam each reached out to APA and were connected through the chair of the Caucus of Asian American Psychiatrists, Dora Wang, M.D. Together, Wong and Anam worked closely with APA’s divisions of Communication and Diversity and Health Equity and co-wrote a new toolkit for reporters that outlines best practices for reporting on the Asian American/Pacific Islander (AAPI) community and explains the mental health impacts of media misrepresentation. They presented the toolkit to the Asian American Journalists Association, then released it to the public last November.“Media is a core part of how we learn about the world around us,” Anam said. “Stereotypes dehumanize communities and allow room for mistreatment. When you subvert stereotypes and portray communities with nuance or as multidimensional characters, it makes a huge difference to people’s internal sense of self and how they may be seen and treated by others.”The toolkit gives journalists practical advice on reporting on the AAPI community, such as including the perspectives of AAPI individuals for diversity in general stories, reminding the audience that the community is deeply rooted in America, and clearly identifying acts of racism. It also emphasizes the importance of people seeing themselves depicted accurately, which “allows them to better relate to health or other messaging.”The toolkit includes startling data points, such as that 84% of Asian Americans say they worry about being the victim of a mass shooting, and more than 20% of Asian Americans say they worry daily or almost daily that they might be threatened or attacked because of their race or ethnicity. Both Anam and Wong described stereotypes and misrepresentations of the APPI communities in media as dangerous because they prime the public to develop discriminatory views, which in turn leads to acts of violence.Media misrepresentation also encourages a sense that members of the APPI community do not belong, Wong said. When the COVID-19 pandemic was initially blamed on China, a significant number of Americans immediately began blaming even their Asian American neighbors. “That’s because they did not view members of the AAPI community as being Americans or belonging,” he explained. “It meant that people like me, a native-born American of Asian descent who has lived here my whole life, were discriminated against.” Further, inaccurate media representations cause the public to see the APPI community as monolithic, rather than the massively diverse group of people that it comprises. “There’s a huge heterogeneity within the cultures, social norms, and social determinants of health,” Anam pointed out. Yet that diversity is rarely portrayed accurately in the media.In addition to Wong and Anam, the co-authors of the toolkit include Ingrid Chen, M.D., chief resident at Kaiser Permanente-Oakland; Divya Chhabra, M.D., a clinical assistant professor in New York University’s Department of Child Psychiatry; and Nikhita Singhal, M.D., a psychiatry resident at the University of Toronto.Anam and Wong encourage fellow APA members who are similarly passionate about an issue to also work with APA leaders and staff. “We were very fortunate to be connected with APA’s staff because they stewarded us through the development of this toolkit,” Anam said. “We worked together as a team.” ■Resource“Reporter Toolkit: Recommendations on Covering the AAPI Community” ISSUES NewArchived

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,191
Score d'incertitude au seuil0,997

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,003
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,004

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,052
Tête enseignante GPT0,397
Écart entre enseignants0,345 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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