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Record W3121590258 · doi:10.1002/nop2.725

Development and validation of the nursing confidence in managing sedation complications scale

2021· article· en· W3121590258 on OpenAlex

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 Open · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicHealth Education and Validation
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsCronbach's alphaSedationExploratory factor analysisConstruct validityConfidence intervalMedicineScale (ratio)Content validityNursingPsychologyClinical psychologyPsychometricsAnesthesiaInternal medicine

Abstract

fetched live from OpenAlex

AIM: To develop the Nursing Confidence in Managing Sedation Complications Scale. DESIGN: A multi-phased approach was used. METHODS: An initial bank of items was created based on the authors' experience and clinical practice guidelines. An expert panel assessed content validity. Exploratory factor analysis was used for item reduction and regression was used to explore construct validity. Responsiveness was evaluated using a pre-test post-test design. RESULTS: Criteria for content validity was met for 34 items. An 18-item, three-factor solution was identified from exploratory factor analysis performed using Nursing Confidence in Managing Sedation Complications Scale scores from 228 nurses. Subscales accounted for 66% of the variance. Cronbach's alpha for the scale (0.95) and subscales was high (>0.85). There were differences (p < .001) in Nursing Confidence in Managing Sedation Complications Scale scores relative to years of experience and work environment. NC-MSCS scores increased significantly from before to after sedation training (mean difference = 31.8; 95% CI = 24.4-39; N = 31).

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.770
Threshold uncertainty score0.350

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 studies0.0000.000
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 head0.166
GPT teacher head0.473
Teacher spread0.307 · 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