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Record W4282974378 · doi:10.1080/01612840.2022.2083737

Strategies Used to Teach Professional Boundaries Psychiatric Nursing Education

2022· article· en· W4282974378 on OpenAlexaff
Andrea E. Thomson, Nadine Smith, Jane Karpa

Bibliographic record

VenueIssues in Mental Health Nursing · 2022
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsBrandon University
Fundersnot available
KeywordsCurriculumNursingPsychologyNurse educationProfessional boundariesFocus groupProfessional developmentNurse educatorMedical educationMedicinePedagogySociology

Abstract

fetched live from OpenAlex

Issues in understanding professional boundaries have been reported amongst student psychiatric nurses. Nursing students are at risk of crossing and violating professional boundaries as they are inexperienced in maintaining therapeutic relationships. Despite these challenges, there was a lack of literature regarding boundary instruction in psychiatric nursing education. Interpretative description was applied as method to answer: What strategies do psychiatric nurse educators utilize to teach professional boundaries to undergraduate students? Eleven educators and nine psychiatric nurses were interviewed. Data was analyzed using an inductive approach. Eight participants also attended a focus group to help refine analysis. Boundary instruction strategies included sharing stories, reflective activities, discussion in clinical settings, role modeling, and role play. Boundary instruction should be incorporated into psychiatric nursing curriculum in a deliberate manner to ensure students are exposed to the topic in meaningful ways throughout their academic career.

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)0.008
metaresearch head score (Gemma)0.031
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 score0.008
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.031
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0030.004
Scholarly communication0.0040.003
Open science0.0020.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.025
GPT teacher head0.437
Teacher spread0.412 · 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

Citations6
Published2022
Admission routes1
Has abstractyes

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