Listening for the sounds of silence: a nursing consideration of caring for the politically tortured
Bibliographic record
Abstract
In 1997 Amnesty International reported that 115 out of 251 countries surveyed practised torture on their citizens. Many of these victims have been forced to flee their country of origin and become refugees in the West, in countries such as Australia, Canada, the UK and the United States. However, torture itself remains an unspoken and covert problem. In addition to the obvious traumatic effects, it may induce shame and dread on the part of the victim. It may be too terrifying and too painful to talk about, and yet may affect every aspect of a person's life (Forrest 1996). All too commonly a victim of torture may pass unnoticed or unrecognised because health care providers do not know how, or may be unwilling to engage with the issue. This paper will examine the implications for nurses of caring for the tortured. It will explore the nature of torture itself, who might be the perpetrators and who might be the victims (always acknowledging that nurses and other health care staff are often both), how victims of torture may present for health care, and the possible subjective perceptions of a torture victim when faced with a Western health care process and understanding. Finally, it will argue for an increase in awareness and sensitivity on the part of all nurses in all health care settings to the needs and sensibilities of victims of torture, and suggest that caring for them is the distillation of everything good nursing practice should be.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.036 | 0.040 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.018 | 0.022 |
| 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".