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Enregistrement W2398706339

Opening Minds: The Mental Health Commission of Canada’s Anti-Stigma Initiative: Listen to These Lessons

2014· article· en· W2398706339 sur OpenAlexvenueaboutno aff
Patrick W. Corrigan

Notice bibliographique

RevueThe Canadian Journal of Psychiatry · 2014
Typearticle
Langueen
DomainePsychology
ThématiqueMental Health Treatment and Access
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésStigma (botany)CommissionMental healthPsychiatryPsychologyMedicinePolitical scienceLaw
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

In the past 10 years, governments and nongovernmental organizations from New Zealand, Australia, Japan, the United Kingdom, the United States, and several other countries have joined Canada in seeking to replace stigma with affirming attitudes of recovery and self-determination. Policy makers from these groups need to carefully heed lessons from the OM effort of the Mental Health Commission of Canada. From these lessons, policy makers learn what works and what does not so they can decide where resources should and should not be directed toward the problem. In brief, the most effective campaigns support local, contact-based programs seeking to replace specific examples of discrimination (at work, school, or health care settings) with affirming attitudes that yield rightful opportunities. Three lessons from this supplement are highlighted here. First, ending discrimination is the goal. As an advocate with lived experience once told me, “It would be nice if people respected me; but at the end of the day, I don’t want them to block my opportunities.” Attitude change is important but the real proof of stigma programs is tearing down the discriminatory behaviours that undermine a person’s life goals. A focus on discrimination highlights the need for targeted approaches. Instead of changing the overall population with generic programs meant to erase prejudice, advocates need to target groups in positions of power visa-vis people with the mental illness label. Anti-stigma efforts need to target employers so they are more likely to hire people with mental illness and provide reasonable accommodations. They need to target health care providers so they offer the same standards of medical practice regardless of mental health experiences. They need to target the educational system so classroom supports help students with mental illness to achieve their academic goals. Second, interaction with people with lived experience is the method. One might think educating the public about myths and facts of mental illness might be an effective way to erase stigma. Results outlined in this Supplement and supported by a recent meta-analysis suggest an alternative.1 Unlike education, contact with people with mental illness has consistently been shown to be more effective in tearing down prejudice and discrimination. Research in the OM series begins to unpack what comprises effective contact. Stories of recovery that contrast one’s mental health challenges with hopeful consequences and personal accomplishments are most effective. These stories should be crafted for the targeted audience; for example, share with employers accomplishments on the job when employees are provided reasonable accommodations. Stories of recovery are also more effective when reflecting local agendas. Messages from Toronto likely have little currency in Saskatoon. Even more, stories from people with European heritage and in recovery have less potency for First Nations members than stories from the First Nations people themselves. OM authors believe strategies need to heed concerns about sustainable effects. Although one interaction between people with lived experience and targeted audiences leads to positive change, benefits will wane if some kind of continued plan for interaction is not included. Swooping in for change does not lead to lasting effects. I found the most important and innovative idea in this series was building networks of practice. These networks nicely combine intentions of maintaining effects and local strategies to erase stigma. When implementing its program, OM searched for existing programs with stable funding to further their anti-stigma agenda. In future work, Stuart and colleagues2 need to elaborate on stable funding, as success is going to require support for the type of programs that have traditionally been underfunded. Ultimately, contact-based approaches to stigma change will be most effective when people with mental illness broadly disclose their history of illness and recovery. Working alongside a person with mental illness, or living in the same apartment building with them, or going to church on Sunday with them is what eventually has the greatest impact on tearing down prejudice. People with mental illness should come out proud with their experiences.3 Still, there are risks, so disclosure decisions should not be pursued cavalierly. Coming Out Proud to erase the stigma of mental illness is a 3-session program meant to help people with these decisions and subsequent disclosures: consider the pros and cons of disclosure, learn strategies to disclose with less risk, and craft a story that helps the people disclosing to meet their individual goals. Preliminary research suggests the program yields positive benefits for participants.4 Third, program evaluation is essential. Advocates for social justice are motivated to tackle the problem now. Unfortunately, such zeal can lead to blind action with little positive, or perhaps even unintended negative, effects. Consider, for example, the substantial data that suggests well-intentioned education programs that framed mental illness as a brain disorder actually worsen stigma.5 While participants in these programs are less likely to blame people for their mental illness, they are also less likely to believe people with mental illness recover. And it is the belief in no recovery that leads to employers, for example, not hiring people with mental illness. Data and evaluation were essential to the OM campaign, and a frank discussion of the difficulty of the research enterprise in this regard. While randomized designs of basic behavioural research have some value, many questions important to stigma change cannot be answered using these kinds of well-controlled approaches. The OM Supplement illustrates benefits of research approaches beyond the randomized controlled trial. Qualitative methods are essential for generating preliminary questions that represent stakeholder interests. Population questions require sampling and representation strategies that are more the realm of sociologists. Econometrics is useful for testing hypotheses regarding the monetary impact of anti-stigma approaches. Central to all these efforts is CBPR. In CBPR, people from an indexed community are full partners in all aspects of the research enterprise. Enlightened social scientists would never conduct research on First Nations people without having them as active members of the research team. Similarly, research on stigma requires an active team that partners scientists and people with lived experience of mental illness. By partnership, we mean ongoing consensus about research questions, hypotheses, methods, data collection, analyses, and interpretation of results. The value of people with lived experience to this partnership is especially evident at start-up and completion. Going into a study, people with lived experience may have unique insight regarding questions and hypotheses. Coming out of the study, researchers typically put completed reports on shelves and move on. People with lived experience use the findings to determine direction for change, and then move in that direction.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,011
score de la tête « metaresearch » (Gemma)0,022
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,097
Score d'incertitude au seuil0,422

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0110,022
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0020,002
Études des sciences et des technologies0,0390,012
Communication savante0,0200,010
Science ouverte0,0040,007
Intégrité de la recherche0,0240,038
Charge utile insuffisante (le modèle a refusé de juger)0,0170,004

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 Opus0,051
Tête enseignante GPT0,364
Écart entre enseignants0,313 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
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

Citations1
Publié2014
Routes d'admission2
Résumé présentoui

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