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Record W2894269352 · doi:10.1136/emermed-2018-208099

A just outcome, or ‘just’ an outcome? Towards trauma-informed and survivor-focused emergency responses to sexual assault

2018· editorial· en· W2894269352 on OpenAlexaff
Elaine J. Alpert

Bibliographic record

VenueEmergency Medicine Journal · 2018
Typeeditorial
Languageen
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineOutcome (game theory)Sexual assaultMedical emergencySuicide preventionPoison controlInjury preventionPsychiatry

Abstract

fetched live from OpenAlex

Sexual assault is one of the most violating and traumatising forms of abuse encountered by emergency department providers. Because the vast majority of sexual assaults are perpetrated by someone known, and possibly even related to the survivor, feelings of profound betrayal and loss of trust can make emergency clinical evaluation, including forensic evidence collection, particularly challenging.1 As described in the EMJ paper by Muldoon and colleagues, the collection of forensic evidence by specially trained clinicians may increase the effectiveness of legal prosecution, and is often assumed to be a ‘just’ or, in other words, desired outcome.2 The decision to obtain, and then process, forensic evidence, however, must be an individual, personal decision made by each and every survivor, informed by their own priorities, values and needs, and supported non-judgementally by emergency healthcare providers acting in a trauma-informed manner. ‘Just’ outcomes are unique for each patient and thus should be defined by each individual sexual assault survivor. The survivor’s decisions can be informed, but should neither be directed nor influenced by healthcare providers, administrators, law enforcement personnel, researchers or any other entity. Organisational policies …

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)0.009
metaresearch head score (Gemma)0.039
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.282
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.039
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0550.000

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 head0.197
GPT teacher head0.486
Teacher spread0.289 · 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.

Study designNot applicable
Domainnot available
GenreEditorial

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

Citations10
Published2018
Admission routes1
Has abstractyes

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