Understanding and Supporting Grief in the Professional Environment
Bibliographic record
Abstract
This symposium contributes to the Academy of Management’s conference theme “Innovating for the Future: Policy, Purpose, and Organizations,” showcasing five papers that highlight the impact of personal and professional grief on work and personal well-being. Loss and grief are inevitable parts of personal and professional life. Despite how common, yet critical grief-related experiences may be in workers’ lives, management scholarship on the topic remains limited in its understanding of (1) the personal beliefs and understandings people within organizations hold about grief and the influence these beliefs may have on the experience of grief at work, (2) the nuanced varieties in grieving employees’ experiences with personal and professional loss contributing to how workers communicate, make sense of, and move forward with their grief and (3) how colleagues, leaders, and organizations can best support grieving employees. This symposium includes mixed methods, qualitative, and quantitative research perspectives that enrich the current understanding of grieving workers’ beliefs and experiences and provides research-based recommendations for organizations in managing and supporting grieving individuals. Together, with discussion led by Professor Olivia “Mandy” O’Neill, these papers aim to provide insights into the processes and outcomes of grief and mourning for employees, their colleagues, and organizations, engage an emerging community of scholars focused on issues related to grief, well-being, and the work-life interface, and generate a strong program of future academic research. How Lay Theories About Grief Influence Grieving Employees’ Work Experiences Author: Yu Tse Heng; U. of Virginia - McIntire School of Commerce Author: Elizabeth E. Stillwell; London School of Economics and Political Science Author: Samantha Dodson; Haskayne School of Business, U. of Calgary Grief in Fertility Treatment: Unintentional Childless Women’s IVF Challenges Author: Lara Bertola; Rennes School of Business Author: Esra Paca; Rennes School of Business Author: Ozlem Ozkok; Rennes School of Business Author: Tunyaporn Vichiengior; Rennes School of Business Author: Julia Roloff; Rennes School of Business Physicians’ worsened mental health in the face of end of life, death, trauma and pathological grief Author: Thibaud Damy; Henri Mondor Hospital, France Author: Rebecca DICKASON; CREM UMR 6211, U. of Rennes Author: Erwan Flécher; Rennes Hospital, France Author: Frédéric Pochard; Famirea Group, France Author: Sophie Provenchère; Bichat Hospital, France Author: Véronique Thoré; Nancy Hospital, France Employee Motivations to Return to Work Following Bereavement Author: Rebecca Cairns; Saint Mary’s U. Author: Stephanie Gilbert; Cape Breton U. Author: E Kevin Kelloway; St. Mary's U. Author: Jennifer Dimoff; Telfer School of Management, U. of Ottawa Author: Jane Mullen; Mount Allison U. Author: Michael Teed; Bishop’s U. An investigation into Employee Experiences of Bereavement Support in the Workplace Author: Emily Allan; Xtra Mile Marketing Author: Oluseyi Aju; Leeds Beckett U.
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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.024 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".