Promoting Meaning‐Making to Help our Patients Grieve: An Exemplar for Genetic Counselors and Other Health Care Professionals
Bibliographic record
Abstract
Genetic counselors and other health professionals frequently meet with patients who are grieving a loss. It is thus helpful for medical professionals to be familiar with approaches to bereavement counseling. Grief theory has evolved over the last few decades, from primarily stage theories of grief such as Kübler-Ross's "five stages of grief" to frameworks that promote more complex and long-term ways to cope with a loss. Herein I present one recent grief theory - meaning-making - and describe how it can be applied to help parents of children with disabilities grieve the loss of the child that they expected. In particular, I describe a scenario that many genetic counselors face - meeting with the parents of a child with Down syndrome. I outline the research done on the reactions, grief and coping experienced by parents in this circumstance, and I present suggestions for encouraging healthy coping and adjustment for parents, based on the meaning-making perspective. The meaning-making theory can also be applied to many of the other losses faced by genetic counseling patients.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.015 |
| Insufficient payload (model declined to judge) | 0.002 | 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".