Bibliographic record
Abstract
More than 80% of employees would rather have good mental health than a high‐paying job, according to a new report released by The Workforce Institute at UKG, which provides research and education on critical workplace issues facing organizations around the world. The institute surveyed 3,400 people across 10 countries to examine the critical role that jobs, leadership, and managers play in supporting mental health within and outside of work, the news release group, Business Wire, reported on Jan. 24. Managers impact employees’ mental health (69%) more than doctors (51%) or therapists (41%) — and even the same as a spouse or partner (69%), the report, “Mental Health at Work: Managers and Money,” stated. At the end of work, 43% of employees are “often” or “always” exhausted, and 78% of employees said that stress negatively impacts their work performance. That stress from work carries into personal lives, as employees said work negatively impacts their home life (71%), well‐being (64%), and relationships (62%), the report indicated. Among people who reported “poor” or “very poor” mental health, around one‐quarter (28%) of them said they lack work‐life balance, compared with just 4% of people in “good” or “excellent” mental health.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.165 | 0.090 |
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".