Bibliographic record
Abstract
Directly or indirectly, each of us has felt the impact of recent economic change, and some of us feel it more than others. Layoffs, tighter budgets, hiring freezes—they’re all adding to increased on-the-job tension. An American Psychological Association survey in 2008 found that eight out of 10 people have “major stress” due to the current economic situation. Mental-health claims are the fastest-growing category of Canadian disability costs, according to the Canada Safety Council. And with all this, many places of employment continue to treat workplace mental illness and as a taboo subject. Mental health is more than just a personal issue—it’s a social issue and a workplace issue. Ignoring mental health in the workplace costs industry billions each year. But mental-health advocates are working to turn that around. “We have to be good employers,” says Lloyd Craig, president and CEO of Coast Capital Savings, “no matter if the economy is good or bad.” He adds that treatment of mental illness is still under-resourced, and too few people are being diagnosed. Since his teenage son’s depression-related suicide in 2001, Craig has become a champion of shining a light on mental health issues in the workplace. “In the developed world, for people between the ages of 15 and 44, depression is the number-one burden of disease,” Craig says. “At Coast Capital Savings, our number-one drug cost is anti-depressants.” Craig counts the introduction of a mental-health survey among employees in 2006 as one of his company’s greatest accomplishments. Staff members shared survey results and discussed them at company workshops. Many were shocked at the number of people feeling depressed and even suicidal at workplace. Bringing people together to talk about how they feel laid the groundwork for being open about it, which eliminates the stigma. Craig believes that we need to understand that the whole person comes to work every day. Someone with a kidney problem can talk about it over coffee. But can someone with depression talk about that over coffee? In tough economic times, companies cut wherever they can. If companies are looking for places to cut, mental health should be the last place they look. Mental health is the ability to meet our obligations and challenges; adapt to change and adversity; share, not hoard; give credit, not blame; relate well to others; and lead by example.
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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.016 |
| Insufficient payload (model declined to judge) | 0.059 | 0.040 |
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".