Evaluation of eMentalHealth.ca, a Canadian Mental Health Website Portal: Mixed Methods Assessment
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".