The Business Case: Collaborating to Help Employees Maintain Their Mental Well-Being
Bibliographic record
Abstract
There has been a change in the mindset of businesses in recent years. Companies are starting to realize that proactively helping their employees to maintain mental health is beneficial, both for their workers and their business. In this article, we present three different but complementary views - those of an advocate, an employer and a provider - on helping employees maintain mental, and physical, health. In the first section, Sari Sairanen outlines programs and services to manage stress and maintain mental health that have been developed by the Canadian Auto Workers' union and implemented in partnership with employers, wellness providers, service agencies and other community partners. The union focuses on raising awareness and providing education, as well as removing the stigma associated with mental illness. Deanna Matzanke, in her section, discusses the commitment of a company, Scotiabank, to create and maintain an inclusive and accessible workplace for all its employees. It has recently worked with providers to develop and implement integrated services dealing specifically with mental health illness and addiction, which aid not only its current employees but also possible future employees. Finally, Doug Smeall shares his observations as an insurer at Sun Life Financial, who has seen the rates of both short-term and long-term disabilities increase. He elaborates on the collaborative work between insurers and employers to help employees maintain their mental health, and to return to work sooner when issues do occur. Ultimately, this article argues that unions, employers and insurers can work together with partners and employees to promote and maintain employee health because, as Sairanen asserts, "preventing a problem in the first place is the best strategy."
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 | 0.015 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.053 | 0.023 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.012 | 0.023 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".