Evaluation of eMentalHealth.ca, a Canadian Mental Health Website Portal: Mixed Methods Assessment
Notice bibliographique
Résumé
BACKGROUND: Many Canadians have mental health needs, and it can be challenging not knowing where to go for mental health information, services, and support. The website eMentalHealth.ca was created to facilitate and assist Canadians to (1) learn about mental health, (2) screen for common mental health issues, and (3) find mental health services and support. OBJECTIVE: The aim of this study was to use multiple methods to learn about visitors of eMentalHealth.ca, their perceptions, and their satisfaction with the website. METHODS: Website analytics (Google Analytics) provided information about the number of unique visits to the website and how the site was used. Web-based self-administered surveys were used to gather additional information on users' characteristics and to assess their perception of the website and satisfaction with the website. RESULTS: Web analytic results showed that from January 1 to December 31, 2017, there were 651,107 users, with 1.97 million page views. Users were more often female than male, and the majority of users were aged 35 years and older. Most users were located in Canada (612,806/651,107, 94.12%), and the most common city of origin of users was Toronto (101,473/651,107, 15.58%), followed by Ottawa (76,692/651,107, 11.78%), and Montreal (26,621/651,107, 4.09%). Web-based surveys were completed by a total of 370 respondents from June to December 2017. Overall, the majority of users were satisfied with the website (93.0%, 320 out of 344 responses). Positive feedback was related to the content of the website as a helpful resource, and negative feedback was related to technical difficulties as well as the design of the main page. This analysis will be used to help the team with ongoing improvements to the website, for example, improving technical issues and homepage usability. CONCLUSIONS: Most visitors reported satisfaction with their use of eMentalHealth.ca to learn about mental health as well as where to find help. Mental health websites such as eMentalHealth.ca are a low-cost way to increase public awareness about mental health.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,010 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,001 |
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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».