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Record W4322771473 · doi:10.1186/s12909-023-04115-5

Validation of student academic advising and counseling evaluation tool among undergraduate nursing students

2023· article· en· W4322771473 on OpenAlexaff
Abeer Selim, Abeer Omar, Shaimaa Awad, Eman Miligi, Nahed Ayoub

Bibliographic record

VenueBMC Medical Education · 2023
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsTrent University
Fundersnot available
KeywordsCronbach's alphaContent validityConstruct validityMedical educationReliability (semiconductor)Internal consistencyNursingMedicinePsychologyPsychometricsClinical psychologyPatient satisfaction

Abstract

fetched live from OpenAlex

BACKGROUND: Academic advising and counseling services support students in achieving their educational outcomes. Unfortunately, there is a paucity of research on academic advising and student-support systems among nursing students. Therefore, the current study aims to develop a student academic advising and counseling survey (SAACS) and measure its validity and reliability. METHODS: Cross-sectional design was used to collect online self-administered data from undergraduate nursing students in Egypt and Saudi Arabia. The SAACS is developed based on relevant literature and tested for content and construct validity. RESULTS: A total of 1,134 students from both sites completed the questionnaire. Students' mean age was 20.3 ± 1.4, and the majority of them were female (81.9%), single (95.6%), and unemployed (92.3%). The content validity index (CVI) of SAACS overall score (S-CVI) is 0.989, and S-CVI/UA (universal agreement) is 0.944, which indicates excellent content validity. The overall SAACS reliability showed an excellent internal consistency with a Cronbach's Alpha of 0.97 (95% CI: 0.966 - 0.972). CONCLUSIONS: The SAACS is a valid and reliable tool for assessing students' experience with academic advising and counseling services and can be utilized to improve those services in nursing school settings.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.646
Threshold uncertainty score0.600

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.043
GPT teacher head0.439
Teacher spread0.396 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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