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Record W2574569003 · doi:10.5430/jnep.v7n6p56

Baccalaureate nursing students’ confidence on patient safety

2017· article· en· W2574569003 on OpenAlexaffvenueabout
June Raymond, Jennifer Medves, Christina Godfrey

Bibliographic record

VenueJournal of Nursing Education and Practice · 2017
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsQueen's University
Fundersnot available
KeywordsPatient safetyNursingMedicinePerceptionNurse educationDescriptive statisticsPsychologyMedical educationHealth carePolitical science

Abstract

fetched live from OpenAlex

High rates of patient safety events led to media attention and litigation that sparked the patient safety movement. Gaining awareness of nursing students’ confidence levels on patient safety will reveal their perceptions of their competency. A quantitative descriptive cross-sectional method was employed. The Health Professional Education in Patient Safety Survey was used to determine how 458 baccalaureate nursing students from two nursing programs in Ontario, Canada rate their confidence levels on patient safety. Nursing students are the most confident on Clinical Safety topics and least confident on topics related to Human and Environmental Factors and Culture of Safety. Overall, they are more confident in the classroom than in the clinical settings with fourth year students expressing lower degrees of confidence on many of the areas. Education programs need to reinforce patient safety and educate students on their responsibilities to protect patients/clients. Programs need to target strategies to reduce student fears, promote error reporting, and system improvements.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.052
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.211
GPT teacher head0.581
Teacher spread0.370 · 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 source (direct Gemma or distilled Codex), 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

Citations13
Published2017
Admission routes3
Has abstractyes

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