MétaCan
Menu
Back to cohort
Record W2099464792 · doi:10.3928/01484834-20060301-07

Preparing Nursing Students to be Health Educators: Personal Knowing Through Performance and Feedback Workshops

2006· article· en· W2099464792 on OpenAlexaffabout
Maureen Little

Bibliographic record

VenueJournal of Nursing Education · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsSelkirk College
Fundersnot available
KeywordsPraxisNurse educationPsychologyNursingPedagogyPersonal developmentMedical educationMedicinePolitical science

Abstract

fetched live from OpenAlex

<H4>ABSTRACT</H4> <P>Newly graduated RNs are expected to be competent health educators for individuals, groups, and communities. To prepare for this complex role, nursing students need time to focus on developing basic teaching skills and self-confidence in a non-threatening learning environment. Of primary importance to novice teachers’ development is taking the time to identify and appreciate the personal dimensions that are an integral part of the health educator role. Carper identified personal knowing as one of the four ways of knowing in nursing. This article describes an innovative praxis strategy that used videotaped performances, learner feedback, and self-reflection to encourage personal knowing in relation to the experience of nursing students learning to teach groups of clients. </P> <H4>AUTHOR</H4> <P>Received: May 14, 2004 </P> <P>Accepted: September 10, 2004 </P> <P>Ms. Little is an Instructor in the Collaboration for Academic Education in Nursing Program, Selkirk College, School of Health and Human Services, Castlegar, British Columbia, Canada. </P> <P>Address correspondence to Maureen Little, MScN, RN, Instructor, Collaboration for Academic Education in Nursing Program, Selkirk College, PO Box 1200, Castlegar, British Columbia, Canada V1N 3J1; e-mail: <A HREF="mailto:mlittle@selkirk.ca">mlittle@selkirk.ca</A>.</P>

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.405
Threshold uncertainty score0.566

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.044
GPT teacher head0.423
Teacher spread0.378 · 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 teacher head, 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

Citations19
Published2006
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Nursing EducationSame topicEducation and Critical Thinking DevelopmentFrench-language works237,207