Workplace and non-workplace loneliness: a cross-sectional comparative study on risk factors and impacts on absenteeism and mental health among employees in Spain
Bibliographic record
Abstract
PURPOSE: The aim of this study is to (1) evaluate prevalences and concordance between workplace and non-workplace loneliness, (2) compare sociodemographic risk factors between workplace and non-workplace loneliness, (3) compare working conditions-related risk factors between the two contexts of loneliness, and (4) compare the impact of workplace and non-workplace loneliness on absenteeism, depression, anxiety and substance use disorder. METHODS: A sample of the employee residing in Spain (n = 5400) was surveyed using computer-assisted web interviews (CAWI) during August and September 2024. Logistic regression models were constructed to compare the effects of risk factors for workplace and non-workplace loneliness (including sociodemographic factors, and factors related to working conditions), as well as the association of workplace and non-workplace loneliness on absenteeism, and symptoms of depression, anxiety, and substance use disorder. RESULTS: Among active workers, 40.7% report experiencing workplace loneliness, while 42.0% report non-workplace loneliness. The level of concordance between both types of loneliness is low (Kappa = 0.36). Both types are more prevalent among younger and immigrant workers. Other sociodemographic risk factors (being female, non-married, and non-heterosexual) were significantly associated with non-workplace loneliness. Meanwhile, risk factors related to working conditions -particularly working under stress and labor precariousness- were associated with both types of loneliness, which showed an independent impact on absenteeism, depression, anxiety, and substance use disorder. CONCLUSION: Most of the social determinants of workplace loneliness are rooted in the work environment, indicating that effective interventions should focus on addressing labor conditions and precariousness to improve both workplace and non-workplace loneliness and their impacts on absenteeism and mental health.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".