Bibliographic record
Abstract
This symposium includes four papers that provide diverse yet complementary perspectives on mental health challenges at work. Whereas many employees face mental health challenges in the course of their employment—underscored by the fact that one in six employees have a mental illness—mental health challenges are persistently under-examined in management research. The papers in this session are diverse in their contextual focus, temporal focus (e.g., short-term challenges versus chronic mental illness) and analytic approach, yet have commonalities that serve to promote a cohesive multifaceted view of this topic. For instance, multiple papers in this session investigate the role of work-related antecedents to mental health challenges and the role of support in helping—or failing to help—those with mental health challenges. Of note, all these studies uncover nuanced and complex dynamics that underscore the futility of merely trying to “fix” employees’ mental health challenges with simplistic interventions. Following presentation of the papers, integrative commentary will be provided that suggests fruitful opportunities for future research on mental health challenges at work. Anger Trajectories and Resilience Among Combat-Deployed Soldiers Presenter: Jason Kautz; U. of Texas at Dallas Presenter: Laura Campbell-Sills; U. of California, San Diego Presenter: Paul Bliese; Darla Moore School of Business, U. of South Carolina Presenter: Robert Ursano; Uniformed Services U. Hidden from View: Leading with Depression and Anxiety Presenter: Sally Maitlis; U. of Oxford Consequences of Work Injuries on Mental Health: The Role of Social Support Presenter: Steve Granger; U. of Calgary Presenter: Nick Turner; U. of Calgary Presenter: Sandy Hershcovis; U. of Calgary Presenter: Patrick Bruning; U. of New Brunswick Social Support of Employees with Mental Illness Presenter: Emily Rosado-Solomon; California State U., Long Beach Presenter: Sherry M. B. Thatcher; U. of South Carolina Presenter: Sam Strizver; U. of South Carolina Presenter: Ron Capistrano; California State U., Long Beach
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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 teacher head, 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".