All the Lonely People: An Integrated Review and Research Agenda on Work and Loneliness
Bibliographic record
Abstract
Decades of studies spanning multiple disciplines have provided insight into the critical role of loneliness in work contexts. In spite of this extensive research, a comprehensive review of loneliness and work remains absent. To address this gap, we conducted a multidisciplinary review of relevant theory and research and identified 213 articles reporting on 233 empirical studies from management, organizational psychology, sociology, medicine, and other domains to uncover why people feel lonely, how different features of work can contribute to feelings of loneliness, and the implications of employee loneliness for organizational settings. This enabled a critical examination of the distinct conceptualizations and operationalizations of loneliness that have been advanced and the theories underpinning this scholarship. We developed a comprehensive conceptual model that integrates cognitive discrepancy theory, the affect theory of social exchange, and evolutionary theory. This model elucidates the core antecedents, mediators, outcomes, moderators, and interventions forming the nomological network of work related loneliness, including cross-level influences within teams and among leaders. Our review also identifies a number of promising areas for future inquiry to improve our understanding and measurement of loneliness, the process of experiencing and managing loneliness in the workplace, and potential interventions to reduce it. Finally, we provide tangible guidance for organizations and practitioners on how to address and mitigate employee loneliness. Ultimately, our review underscores the complex nature of loneliness and work and establishes a foundation for advancing both scholarly discourse and organizational practices in this critical domain.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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, 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".