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Record W347083555

Transforming Nursing Education through Problem-Based Education

2001· article· en· W347083555 on OpenAlexaboutno aff
Mary K. Alexander

Bibliographic record

VenueNursing and Health Care Perspectives · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicProblem and Project Based Learning
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumNurse educationProblem-based learningMedical educationNursing researchNursingRelevance (law)MedicinePsychologyPedagogyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

Transforming Nursing Education Through Problem-Based Education by Elizabeth Rideout, PhD, RN; Sudbury, MA: Jones and Bartlett, 2001; 345 pages; $25.00 This book represents a collaborative project of faculty members at the McMaster University School of Nursing where problem-based learning (PBL) has been used in all nursing courses since the mid-- 1970s. Thus, the information presented includes the experiences of the faculty authors as well as an analysis of the literature. The beginning chapters make the case for the importance of PBL in nursing education. Conclusions drawn from the reviewed literature indicate that students from conventional curricula did better on standardized examinations than did those from PBL programs, but learners from PBL curricula were rated somewhat better in their clinical performance and showed increased library use. It is interesting to note that of the five programs cited in this book for embodying positive attitudes and behaviors in using PBL, three are in medical schools. One is an interdisciplinary program, and one is an undergraduate nursing program. Several chapters have particular relevance for faculty in undergraduate nursing education, but this book is an excellent reference for graduate nursing faculty as well. In fact, it contains three chapters that provide specific information regarding applications of PBL nursing in master's programs around the world. The author assesses the research evidence on PBL outcomes in graduate nursing programs and forecasts the integration of PBL and electronic technologies. While at first, I had some concern about references to the use of methodologies of pedagogy rather than andragogy, or adult learning, the four authors of Chapter 3, Facilitating Self-Directed Learning, refer to Knowles' definitions of andragogy as central to self-- directed learning. Examples of methods by which faculty can help students learn to do self-assessment of learning needs and self-- evaluation are provided. An overview of small-group learning contains useful information for students and faculty for whom this would be a new experience. Examples of group assignments for beginning students and criteria for individual evaluation of the group process are provided. The faculty role in information management - the development of skills and the need to be a role model in carrying them out - is stressed. Although research findings regarding the application of reflection in nursing can be interpreted as either effective or questionable, reflection and reflective practice are described as basic to PBL in nursing education. The authors note that reflection has been adopted as a mandatory component of nursing practice and registration by the United Kingdom Central Council for Nursing and by the College of Nurses of Ontario Canada. …

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0060.004
Open science0.0010.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0090.003

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 head0.036
GPT teacher head0.428
Teacher spread0.392 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations0
Published2001
Admission routes1
Has abstractyes

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