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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.870
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.046
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