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Record W3101211939 · doi:10.5430/jnep.v11n3p11

State of pathophysiology in undergraduate nursing education: A systematic review

2020· review· en· W3101211939 on OpenAlexvenueno aff
Renee Colsch, Suzanne Lehman, Katherine Tolcser

Bibliographic record

VenueJournal of Nursing Education and Practice · 2020
Typereview
Languageen
FieldNursing
TopicNursing Diagnosis and Documentation
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSystematic reviewPathophysiologyPatient careMedical educationMEDLINEPsychologyNursingInternal medicine

Abstract

fetched live from OpenAlex

Background: Nurses who understand pathophysiology can provide higher quality patient care. Various pedagogical strategies make it unclear which practice meets the challenges of teaching pathophysiology. The aim of this systematic review was to synthesize research in the last ten years to report the current state of pedagogical strategies related to teaching pathophysiology concepts in undergraduate nursing.Methods: A systematic review of mixed, quantitative, and qualitative literature guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines was conducted from 2010 to 2020 through electronic databases.Results: Seven studies met the inclusion criteria. A gap exists among comparable research designs, pedagogy strategies, and outcomes specific to undergraduate nursing pathophysiology courses. Conclusions: Findings suggest that more rigorous research designs with validated measurement instruments are needed to compare student satisfaction and outcomes after different pedagogical strategies are applied to undergraduate pathophysiology courses. Also, there is a need to elicit findings related to the retention and effectiveness of pathophysiology concepts in clinical practice.

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 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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.720
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.080
GPT teacher head0.480
Teacher spread0.401 · 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 teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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

Citations7
Published2020
Admission routes1
Has abstractyes

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