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Record W2776563051 · doi:10.5539/gjhs.v10n1p156

A Study of Nurses' Internal, External, and Social Images as Perceived by College Students in South Korea

2017· article· en· W2776563051 on OpenAlexvenueno aff
Heun-Keung Yoon

Bibliographic record

VenueGlobal Journal of Health Science · 2017
Typearticle
Languageen
FieldNursing
TopicHealthcare Education and Workforce Issues
Canadian institutionsnot available
Fundersnot available
KeywordsDescriptive statisticsPsychologyPositive correlationTest (biology)Applied psychologySocial psychologyMedicineStatisticsMathematics

Abstract

fetched live from OpenAlex

PURPOSE: This study is a descriptive survey study aiming to examine the internal, external, and social images of nurses as perceived by Korean college students and clarifying the relationship between these images.METHODS: This study was performed using a structured questionnaire from May 2 to 14, 2016. Data were collected from 221 college students and were statistically interpreted using t-test, one-way ANOVA, and Pearson's correlation coefficient.RESULTS: The study results are as follows: First, the internal image of nurses was scored at 3.46 points, the external image at 2.78, and the social image at 2.76. Second, a significant positive correlation was found between the internal, external, and social images as well as between the external and social images, implying the importance of both internal and external images of nurses.CONCLUSION: The results of this study are expected to be used as a reference base for devising strategies and measures for the enhancement of internal, external, and social images of nurses for future healthcare consumers.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.611

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.041
GPT teacher head0.453
Teacher spread0.413 · 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 designObservational
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

Citations0
Published2017
Admission routes1
Has abstractyes

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