The Potential for Workplaces to Provide Social Support for Distressed Infrastructure Workers
Bibliographic record
Abstract
Infrastructure workers experience high rates of psychological distress and suicide. Social capital (e.g., co-workers, friends, family) and social support (e.g., emotional, practical, informational) help to minimize distress. This study explores how social capital and social support contribute to psychological distress and if accessing social capital to provide social support is different for distressed compared to non-distressed workers. A sample of 220 infrastructure workers recruited online from Canada, the United Kingdom, and the United States of America was used. The study explored social capital (sum and diversity) along with social support and who the workers would approach first for each type of social support. It found that increased social capital was associated with higher distress, whereas lower social support was associated with higher distress. The primary contribution of this research indicates that although distressed infrastructure workers have more social capital available, they may not be obtaining the necessary social support needed from their networks. Also, as some distressed workers indicated they approach work colleagues to receive some types of social support, there may be an opportunity for workplaces to provide social support to co-workers to alleviate the gap in support and help improve psychological well-being.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".