Mental illness, drinking, and the social division and structure of labor in the United States: 2003‐2015
Bibliographic record
Abstract
BACKGROUND: We draw on a relational theoretical perspective to investigate how the social division and structure of labor are associated with serious and moderate mental illness and binge and heavy drinking. METHODS: The Panel Study of Income Dynamics and the Occupational Information Network were linked to explore how occupation, the productivity-to-pay gap, unemployment, the gendered division of domestic labor, and factor-analytic and theory-derived dimensions of work are related to mental illness and drinking outcomes. RESULTS: Occupations involving manual labor and customer interaction, entertainment, sales, or other service-oriented labor were associated with increased odds of mental illness and drinking outcomes. Looking for work, more hours of housework, and a higher productivity-to-pay gap were associated with increased odds of mental illness. Physical/risky work was associated with binge and heavy drinking and serious mental illness; technical/craft work and automation were associated with binge drinking. Work characterized by higher authority, autonomy, and expertise was associated with lower odds of mental illness and drinking outcomes. CONCLUSIONS: Situating work-related risk factors within their material context can help us better understand them as determinants of mental illness and identify appropriate targets for social change.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".