[Association between work exposure to neurotoxic substances with workers' relationships with their social network].
Bibliographic record
Abstract
BACKGROUND: This study explored the impact on family life and social relations that may result from symptoms associated with exposure to neurotoxic substances in the workplace. We assessed the associations between exposure to neurotoxic substances in the workplace, workers'mental health, and workers'relationships with their social network. METHODS: A sample of 53 workers and their spouse completed a series of questionnaires, an interview on work history, and a structured interview assessing their personal relationships. Exposure to neurotoxic substances in the workplace were assessed by an interview, using a semiquantitative classification system. Mental health was measured with the Profile of Mood States (POMS), and marital satisfaction with the Marital Adjustment Test (MAT). The social network's characteristics were assessed with the Northern California Community Study Interview Schedule (NCCS). The associations between exposure and social networks were assessed with regression analyses. RESULTS: There were no associations between exposure and marital satisfaction. However, we found a negative association between workers'exposure and degrees of overlap between husbands' and wives' social networks (Pearson's correlation r=-0.27; p<0.05) and a positive association between exposure and workers' dependency on their support network (r=0.46; p<0.01). CONCLUSION: The results are discussed in terms of variables potentially linking exposure to social relationships as well as in terms of couples' vulnerability to marital distress among exposed workers.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".