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Record W2053869009 · doi:10.2202/1548-923x.1727

A Narrative Study of the Experiences of Student Nurses Who Have Participated in the Hearing Voices that are Distressing Simulation

2009· article· en· W2053869009 on OpenAlexaff
Jane E. Wilson, Wendy Azzopardi, Shelley Sager, Brian Gould, Sherrill Conroy, Patricia E. Deegan, Suzanne Archie

Bibliographic record

VenueInternational Journal of Nursing Education Scholarship · 2009
Typearticle
Languageen
FieldPsychology
TopicCommunication in Education and Healthcare
Canadian institutionsMcMaster UniversityUniversity of WaterlooUniversity of AlbertaConestoga College
Fundersnot available
KeywordsDistressingNarrativeThematic analysisPsychologyExperiential learningNarrative inquiryNurse educationSample (material)Medical educationNursingQualitative researchApplied psychologyMedicinePedagogyVisual arts

Abstract

fetched live from OpenAlex

The aim of this study was to provide nursing students with an experiential learning opportunity which simulated living with the challenge of voice hearing. The purpose was to access understanding and insights of nursing students who completed "Hearing Voices that are Distressing: A Training Experience and Simulation for Students" (Deegan, 1996). Using a narrative research design and a convenience sample of 27 nursing students, participants were asked to respond in written format to three open ended prompts immediately following their participation in the simulation. Data generated was subjected to a thematic content analysis using a manual cut and paste approach to inductively find meanings and insights elicited from the respondents' actual words. Affirmed in this study was the use of this teaching tool to assist the students in their understanding of the challenges posed by voice hearing.

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)0.006
metaresearch head score (Gemma)0.017
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 score0.007
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0070.005
Scholarly communication0.0050.004
Open science0.0010.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.292
GPT teacher head0.580
Teacher spread0.288 · 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

Citations44
Published2009
Admission routes1
Has abstractyes

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