Navigating Grief in Academia: Prioritizing Supports for Women Scholars through Informed Approaches
Bibliographic record
Abstract
Abstract In this paper, the researchers investigate the unique challenges faced by women, particularly those in academia, who are grieving the loss of a loved one. While academic institutions commonly offer bereavement support services for employees and students, these programs do not often address the specific needs of grieving women after a major loss. Many women find themselves navigating the process of returning to work and resuming their responsibilities alone after a significant loss. Additionally, the psychological, emotional, and practical support offered by colleagues varies widely for bereaved woman academics. Although the importance of better supporting women in academia has been recognized, there is a lack of knowledge and understanding about the tailored support grieving women require. In this paper, the researchers present a comprehensive review of existing literature on grief, bereavement, and their significant impacts on women in academia. Additionally, current organizational policies and academic institution bereavement support programs will be examined. Based on findings, the authors will develop an evidence-based framework designed to enhance support for grieving women in academia, with consideration of aspects of diversity and inclusion Moreover, preliminary training content to educate colleagues on providing compassionate and flexible support to women academics during times of bereavement will be proposed.
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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 | 0.019 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".