Easing the disruption of COVID-19: supporting the mental health of the people of Canada—October 2020—an RSC Policy Briefing
Bibliographic record
Abstract
The COVID-19 pandemic has had a significant impact on the mental health of the people of Canada. Most have found it challenging to cope with social distancing, isolation, anxiety about infection, financial security and the future, and balancing demands of work and home life. For some, especially those who have had to face pre-existing challenges such as structural racism, poverty, and discrimination and those with prior mental health problems, the pandemic has been a major impact. The Policy Briefing Report focuses on the current situation, how the COVID-19 pandemic has exacerbated significant long-standing weaknesses in the mental health system and makes specific recommendations to meet these challenges to improve the well-being of the people of Canada. The COVID-19 pandemic has had a detrimental effect on mental health of people in Canada but the impact has been variable, impacting those facing pre-existing structural inequities hardest. Those living in poverty, and in some socially stratified groups facing greater economic and social disadvantage, such as some racialized and some Indigenous groups and those with preexisting mental health problems, have suffered the most. Some occupational groups have been more exposed to the virus and to psychological stress with the pandemic. The mental health care system was already overextended and under resourced. The pandemic has exacerbated the problems. The care system responded by a massive move to virtual care. The future challenge is for Canada to strengthen our knowledge base in mental health, to learn from the pandemic, and to provide all in Canada the support they need to fully participate in and contribute to Canada’s recovery from the pandemic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.007 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.036 | 0.022 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".