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Record W2600735804 · doi:10.1097/jfn.0000000000000142

The Impact of a Violent Physical Assault on a Registered Nurse: Her Healing Journey and Return to Work

2017· article· en· W2600735804 on OpenAlexaff
Holly Graham

Bibliographic record

VenueJournal of Forensic Nursing · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicWorkplace Violence and Bullying
Canadian institutionsUniversity of Saskatchewan
FundersU.S. Department of Veterans Affairs
KeywordsForensic nursingPosttraumatic stressOccupational safety and healthSuicide preventionNursingWorkplace violencePoison controlInjury preventionMedicineHuman factors and ergonomicsPsychologyPsychiatryMedical emergency

Abstract

fetched live from OpenAlex

Healthcare practitioners are at an increased risk for workplace violence. What happens to a practitioner after a devastating physical assault from a patient in the workplace? This case report describes the impact of a violent assault on a registered nurse and her healing journey and return to the workplace. The Department of Veteran Affairs/Department of Defense "Clinical Practice Guidelines for Posttraumatic Stress Disorder" outline three categories of risk factors that are associated with the development of posttraumatic stress disorder: pretraumatic factors, peritraumatic or trauma-related factors, and posttraumatic factors. Each of these risk factors can contribute to the likelihood of an individual developing posttraumatic stress disorder after a traumatic incident and will be used to frame the discussion of this case. The registered nurse gave her full and informed consent for the author to tell her story.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0070.002
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.046
GPT teacher head0.408
Teacher spread0.361 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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

Citations8
Published2017
Admission routes1
Has abstractyes

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