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Record W4307044616 · doi:10.1097/tme.0000000000000427

Current Practices and Nurse Readiness to Implement Standardized Screening for Commercially and Sexually Exploited Individuals in Emergency Departments in Western Washington Hospitals

2022· article· en· W4307044616 on OpenAlexaff
Johanna Hulick, Lauren Jensen, Abigail Mihaiuc, Ruth Hyewoo Shin, Stephen B. Pope, Sarah Gimbel

Bibliographic record

VenueAdvanced Emergency Nursing Journal · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSex work and related issues
Canadian institutionsGimbel Eye Centre
Fundersnot available
KeywordsMedicineEmergency departmentCLARITYIdentification (biology)NursingHealth careBest practiceMedical emergencyFamily medicine

Abstract

fetched live from OpenAlex

Approximately 80% of trafficked individuals access health care during their victimization. The emergency department is a frontline of care. This study examines current screening practices of Western Washington emergency department nurses to determine nurse and facility readiness for improved identification. Interviews were conducted with nurses to understand their current screening practices for identifying potential victims of sexual exploitation; the acceptability of existing screening questions for use in their settings; and their opinions about the utility of a standardized screening tool for identifying victims and improving care. There is an absence of formal protocols and screening tools, limited clarity regarding roles and responsibilities in the identification and care of trafficked persons among the health care team, and a desire to provide improved care quality to patients. Standardized processes and screening may lead to more efficient and effective identification of, care for, and linkage to vital support services and resources.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.314
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.044
GPT teacher head0.428
Teacher spread0.384 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations4
Published2022
Admission routes1
Has abstractyes

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