Understanding Stigma: A Pooled Analysis of a National Program Aimed at Health Care Providers to Reduce Stigma towards Patients with a Mental Illness
Notice bibliographique
Résumé
Background and Objectives The problem of mental illness-related stigma within healthcare is an area of increasing attention and concern. Understanding Stigma is an anti-stigma workshop for healthcare providers that uses social contact as a core teaching element, along with educational and action-oriented components. The objective of our study was to determine the impact of this program on healthcare providers’ attitudes and behavioural intentions towards patients with a mental illness, and also to ascertain whether various participant and program characteristics affected program outcomes. Our paper reports the results of a pooled analysis from multiple replications of this program in different Canadian jurisdictions between 2013 and 2015. Material and Methods We undertook a pooled analysis of six separate replications of the Understanding Stigma program. All program replications were evaluated using a non-randomized quasi experimental pre- post- follow-up design. The Opening Minds Scale for Health Providers (OMS-HC) was used as the main assessment tool. Study-level and individual-level meta-analysis methods were used to synthesize the data. First, the ‘metan’ command was used to show outcomes by study, using a forest plot. Then, a pooled dataset was produced and analyzed using a random intercept linear mixed model approach with each program being modelled as a random effect. Program and participant characteristics were examined as independent variables using this approach. These were each entered individually. Individual tests included pre to post change by program version (original or condensed), by occupation (nurses versus other healthcare providers), by gender, age, and previous diagnosis of a mental illness. Results Program effect sizes ranged from .19 to .51 (Cohen’s d), with an overall combined effect size of .30. The results of the mixed model analysis showed the improvement from pre to post intervention was statistically significant for the total scale and subscales. Analysis of program and participant factors found that version type, healthcare provider type, gender, and previous diagnosis of a mental illness were all non-significant factors on program outcomes. A significant inverse association was revealed between increasing age and score change. Results also showed a significant positive linear relationship between baseline score and improvement from pre to post intervention. Maintenance of scores at follow-up was observed for participants who attended a booster session. Conclusions The results are promising for the effectiveness of this brief intervention model for reducing stigmatizing attitudes and improving behavioural intentions among nurses and other healthcare providers.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,042 | 0,101 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,008 | 0,031 |
| Bibliométrie | 0,005 | 0,006 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».