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Psychometric Testing of a Theory-Based Measure to Evaluate Clinical Performance of Nursing Students

2021· article· en· W3199689748 on OpenAlex
Mohamed El Hussein, Matthew J. W. McLarnon, Olive Fast

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNursing Education Perspectives · 2021
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsMount Royal UniversityCalgary Laboratory Services
Fundersnot available
KeywordsReliability (semiconductor)FeelingPsychologyScale (ratio)Content validityTest (biology)Internal consistencyValidityPsychometricsClinical psychologyApplied psychologySocial psychology

Abstract

fetched live from OpenAlex

AIM: The aim of this study was to psychometrically test a clinical evaluation tool that measures instructors' gut feelings for placing students on a learning contract. BACKGROUND: Evaluators feel unprepared or hesitant to fail students who do not meet professional and clinical expectations. METHOD: A multiphase process was used to determine the reliability and validity of the Gut Feelings Scale. The first phase focused on item generation, the second phase focused on content validity and feedback from expert raters, and the third phase focused on psychometric evaluation to streamline the item pool and explore validity. RESULTS: Correlations and descriptive statistics for each subscale were calculated. Reliability analyses revealed relatively strong estimates of internal consistency; specifically, the reliability estimates surpassed our criteria of >.70. CONCLUSION: This pilot study established the validity and reliability of the scale and found it to be a reliable tool to guide instructors' evaluative decision-making.

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.

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)0.003
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 score0.865
Threshold uncertainty score0.956

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
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 head0.098
GPT teacher head0.467
Teacher spread0.369 · 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