Book Review: The Grieving Brain: The Surprising Science of How we Learn from Love and Loss
Bibliographic record
Abstract
The Grieving Brain by Mary Francis O'Connor, Ph.D. provides a fascinating overview of her personal experiences as a grieving daughter and clinical scientist, along with neuroscience research, clinical anecdotes, and animal studies on what happens to our brains when we grieve.Acknowledging that the research is still in its infancy she describes her development as a researcher setting out to understand the process of grieving.Using clear prose, she synthesizes and integrates the evolution of her work with that of other neuroscience and bereavement researchers, providing insights and practical guidance.In the "Introduction" she notes that great descriptions are found in art, literature, and poetry and written into the scientific literature about the "what" of grief-what it feels like, what problems it causes, even what the bodily reactions are" (p.x).She always wanted to understand the why of grief, why does it hurt so much?She felt the why of grief hurting so much was in the brain, "If we could look at it from the perspective of what the brain is doing during grief, perhaps we could find the how, and that would help us to understand the why." (p.x).Her experience of her mother's diagnosis of breast cancer when she was 13, while her parents were going through a divorce, and her mother's complicated illness and death when O'Connor was 26 led to her desire "…to understand my mother's grief and pain in retrospect, and to learn what I could have done to help her" (p.xi).She sees grieving as learning to lead a meaningful life without the deceased and hopes that seeing grieving as learning, necessitating rewiring the brain, will help to understand the process of grief.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.026 | 0.014 |
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".