A Child's Death: Lessons from Health Care Providers' Texts
Bibliographic record
Abstract
This article originates from a research study that explores 'what happened' to a 10-year-old child with Rett syndrome, who died from "severe malnutrition" according to a Coroners Service inquest jury. The inquest evidence analyzed, using institutional ethnography, shows that approximately one week prior to this child's death three health care providers (an emergency physician, a hospice volunteer and a home care nurse) conducted individual assessments of the child. Child protection workers were also involved. Textual analysis of the health care providers' records shows how the child was officially and textually constructed as 'dying from a terminal illness' in contrast to the subsequent Coroners Service finding. The authors argue that although professional and organizational texts are a routinely 'taken for granted' component of professional practice, they need to be understood as active in the relations of care or service provision. The article supports this argument by demonstrating how the home care nurse's response to the child was textually coordinated with the other two health care providers' actions and how this coordination resulted in the 'proper' enactment of a Do Not Resuscitate order, leading to courses of action or inaction resulting in the child's death. The lesson offered highlights the problems that can arise when textual realities routinely are given authoritative status and displace other forms of knowing in health care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".