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Record W2140367452 · doi:10.5430/jnep.v3n4p1

Using quality and safety education for nurses principles to enhance foundational nursing courses: Outcomes from an innovative curriculum project

2012· article· en· W2140367452 on OpenAlexvenueno aff
Susan M. McLennon, Barbara Manz Friesth, Sue Lasiter

Bibliographic record

VenueJournal of Nursing Education and Practice · 2012
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumPsychomotor learningCompetence (human resources)Patient safetyMedical educationFocus groupHealth careNurse educationContext (archaeology)PsychologyProfessional developmentNursingMedicinePedagogySociologyPolitical science

Abstract

fetched live from OpenAlex

Background: Shortcomings of traditional methods used to prepare undergraduate nursing students for the increasingly complex demands of the current healthcare environment have been cited by national organizations as rationale for the revision of nursing program curricula. In this curriculum project, two undergraduate nursing courses, in which students were simultaneously enrolled, were enhanced to meet emerging national standards for nursing education. Objectives: The aims of this project were to: 1) integrate the didactic and psychomotor skill components of the two courses into congruent modules with a focus on application in complex health care settings, 2) increase the emphasis on self-directed and collaborative learning to prepare students for inter-professional practice, and 3) implement learning experiences that incorporate the knowledge, skills, and attitudes necessary for professional role development. Methods: Guided by recommendations from the Quality and Safety Education for Nurses project and the American Association of Colleges of Nursing document Essentials for Baccalaureate Education, several enhancements were implemented. First, enhancements were piloted in the fall semester with a small group of students enrolled in the two courses. In the following semester, the full project was implemented for all students (n=126) and course faculty (n=12). Specific strategies included the development of a set of 24 proprietary skills videos, a group cultural competence project, an unfolding case study, and adding clinical situations to support learning in context in the classroom and during psychomotor skills training. Outcomes were evaluated with student and faculty surveys and a faculty focus group. Descriptive statistics were used to summarize survey data and a content analysis was performed on data from the focus group. Results: Overall, the faculty and students evaluated the curriculum enhancements positively with 57% to 88% rating most of the items as “strongly agree” or “agree”. From the focus group, faculty perceived that the enhancements were “more organized…it just flowed”, and “it made clinical a little easier. Faculty thought students were “taking more ownership…of their own learning.” Implications and Conclusions: Students and faculty thought that course changes encouraged self-directed learning and successfully merged clinical application with didactic and psychomotor skills classes. These findings may be useful for others planning course changes that enhance self-directed learning, and that want to merge clinical application within the didactic and psychomotor skills classes. Future research to examine changes in student knowledge, skills, and attitudes as a result of educational initiatives, at all levels of the nursing curriculum is recommended.

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)0.010
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0020.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.310
GPT teacher head0.616
Teacher spread0.305 · 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

Citations2
Published2012
Admission routes1
Has abstractyes

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