MétaCan
Menu
Back to cohort
Record W2526983915 · doi:10.1177/2333393617734510

Exploring Nurses’ Knowledge and Experiences Related to Trauma-Informed Care

2017· article· en· W2526983915 on OpenAlexaff
Yehudis Stokes, Jean Daniel Jacob, Wendy Gifford, Janet E. Squires, Amanda Vandyk

Bibliographic record

VenueGlobal Qualitative Nursing Research · 2017
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsOttawa HospitalChildren's Hospital of Eastern OntarioUniversity of Ottawa
Fundersnot available
KeywordsTrauma careNursingContext (archaeology)MedicineQualitative researchParallelsHealth careNursing carePsychologyMedical emergency

Abstract

fetched live from OpenAlex

Trauma-informed care is an emerging concept that acknowledges the lasting effects of trauma. Nurses are uniquely positioned to play an integral role in the advancement of trauma-informed care. However, knowledge related to trauma-informed care in nursing practice remains limited. The purpose of this article is to present the results of a qualitative study which explored nurses' understandings and experiences related to trauma-informed care. Seven semistructured interviews were conducted with nurses and four categories emerged from the analysis: (a) Conceptualizing Trauma and Trauma-Informed Care, (b) Nursing Care and Trauma, (c) Context of Trauma-Informed Care, and (d) Dynamics of the Nurse-Patient Relationship in the Face of Trauma. These findings highlight important considerations for trauma-informed care including the complex dynamics of trauma that affect care, the need to push knowledge about trauma beyond mental health care, and noteworthy parallels between nursing care and trauma-informed care.

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)0.016
metaresearch head score (Gemma)0.042
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.042
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0080.015
Scholarly communication0.0070.006
Open science0.0020.011
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0020.000

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 head0.398
GPT teacher head0.596
Teacher spread0.198 · 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 designQualitative
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

Citations81
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueGlobal Qualitative Nursing ResearchSame topicChild Abuse and TraumaFrench-language works237,207