Canadian perspectives of digital mental health supports: Findings from a national survey conducted during the COVID-19 pandemic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Objectives: The impact of the COVID-19 pandemic on population mental health has highlighted the potential for digital mental health to support the needs of those requiring care. This study sought to understand the digital mental health experiences and priorities of Canadians affected by mental health conditions (i.e. seekers, patients, and care partners). Methods: A national cross-sectional electronic survey of Canadians was administered through a market research firm's survey panel. Seekers, patients, and care partners were asked about their digital mental health experiences (e.g. uptake, barriers to access) and priorities. Survey responses were summarized using descriptive statistics. Results: Overall, 1003 participants completed the survey. 70.2% of participants routinely use digital mental health supports to support themselves or those they care for; however, only 28.6% of participants are satisfied with the available digital mental health supports. Most participants (73.3%) have encountered some barriers when accessing digital mental health supports. Awareness of digital mental health supports was a top barrier identified by participants. The top digital mental health priorities consisted of digital mental health curation, navigation, and a digital mental health passport. Conclusions: Most participants use digital mental health supports for themselves or others, however, many are unaware of digital mental health supports available. Efforts to improve navigating access to digital and in-person mental health services are seen as a top priority, highlighting the need to enable seekers, patients, and care partners to find the appropriate support and make decisions on how to best improve their mental health.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 it