Bibliographic record
Abstract
Mental health‐related employee leaves continue to skyrocket among U.S. workers, according to new data released by ComPsych Corporation, the world's largest provider of mental health and absence management services, online news agency businesswire reported on Aug. 1. A sample analysis of ComPsych's absence book of business, which covers more than six million people, found that in the first quarter of 2024, more than one in 10 (11%) of all leaves of absence were due to mental health. This represents a 22% increase in mental health leaves versus those taken in the first quarter of 2023. This trend is being driven by female workers, who accounted for 69% of all mental health leaves of absence in 2023, and 71% of all mental health leaves in the first quarter of 2024. “Working women — especially moms and other caregivers — often neglect their self‐care until they hit the point of being so burnt out, they need to take a leave of absence,” said Dr. Jennifer Birdsall, clinical director of ComPsych. “The more [that] organizations can support resiliency‐building, teach self‐care and [ways of] prioritizing work‐life balance before things escalate into significant symptoms with functional impacts, the better. This is where the continuum of care, which includes prevention, comes into play.”
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".