Loving your child to death: Considerations of the care of chronically ill children and euthanasia in Emil Sher's Mourning Dove
Bibliographic record
Abstract
How do parents cope when their child is ill or dying, and when he or she is experiencing constant pain or suffering? What do parents think of the contributions that medical professionals make to the care of their chronically or terminally ill child? Is it possible for a parent to love a child so much that they wish their child to be dead? The purpose of the present paper is to explore these questions and aspects of the care of chronically or terminally ill children using Mourning Dove's portrayal of one family's attempt to care for their ill daughter. Mourning Dove, a play written by Canadian playwright Emil Sher, was inspired by the case of Saskatchewan wheat farmer Robert Latimer who killed his 12-year-old daughter, Tracy, who suffered with cerebral palsy and had begun to experience tremendous pain. Rather than focusing on the medical or legal aspects of the care of a chronically ill child, the play offers a glimpse into how a family copes with the care of such a child and the effect the child's illness has on the family. The reading and examination of nonmedical literature, such as Mourning Dove, serves as a useful means for medical professionals to better understand how illness affects and is responded to by patients and their families. This understanding is a prerequisite for them to be able to provide complete care of children with chronic or terminal illnesses and their families.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.018 | 0.016 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".