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Listening for the sounds of silence: a nursing consideration of caring for the politically tortured

2000· review· en· W1972373864 on OpenAlexaboutno aff
Twilla Racine‐Welch, Mark Welch

Bibliographic record

VenueNursing Inquiry · 2000
Typereview
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsnot available
Fundersnot available
KeywordsTortureShameSilenceCovertNursingRefugeeActive listeningHealth careMedicineDignityAmnestyCriminologyPsychologyHuman rightsPolitical scienceLawSocial psychologyPsychotherapistAesthetics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.933
Threshold uncertainty score0.763

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.153
GPT teacher head0.464
Teacher spread0.311 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations7
Published2000
Admission routes1
Has abstractyes

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