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Record W2768043049 · doi:10.33151/ajp.14.4.510

Paramedic Identification and Management of Victims of Intimate Partner Violence: A Literature Review

2017· review· en· W2768043049 on OpenAlexaboutno aff
Breanna Mackey

Bibliographic record

VenueAustralasian Journal of Paramedicine · 2017
Typereview
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsnot available
Fundersnot available
KeywordsDomestic violenceConsistency (knowledge bases)MedicineInclusion (mineral)Poison controlMedical emergencySuicide preventionIdentification (biology)PopulationHuman factors and ergonomicsPsychologyFamily medicineSocial psychologyEnvironmental health

Abstract

fetched live from OpenAlex

Introduction Intimate partner violence (IPV) occurs between adults of the same or opposite sex in a current, or past, intimate relationship. The aim of this paper is to review the literature regarding paramedic confidence, capacity and accuracy when identifying adult victims of IPV and subsequent management of the scene when IPV is suspected or identified. Methods A review of the literature using Ovid MEDLINE was conducted; five articles met the inclusion and exclusion criteria. Results Results show a consistency in findings across research areas in Australia, Canada and the United States and are clear in four separate areas: paramedics demonstrate a high degree of accuracy in identifying IPV victims; professional training effectively increases paramedic knowledge of IPV; greater than 50% of the paramedic population surveyed felt underprepared to deal with an IPV scene; and the majority of surveyed paramedics attend between one and 10 IPV scenes per year. Conclusion This review indicates that paramedics have the capacity to accurately identify IPV victims, and that paramedics recognise a deficit in their professional IPV training. Further research is required, using a robust sample size, to construct appropriate training packages and guide improvement to paramedic clinical practice guidelines.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.892
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.068
GPT teacher head0.441
Teacher spread0.373 · 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 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

Citations4
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueAustralasian Journal of ParamedicineSame topicIntimate Partner and Family ViolenceFrench-language works237,207