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Record W3169940955 · doi:10.11124/jbies-20-00086

Physical assessment skills taught in nursing curricula: a scoping review

2021· review· en· W3169940955 on OpenAlexaff
Sherry Morrell, Natalie Giannotti, Gina Pittman, Adam Mulcaster

Bibliographic record

VenueJBI Evidence Synthesis · 2021
Typereview
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsUniversity of WindsorCentre for Excellence in Mining Innovation
Fundersnot available
KeywordsCurriculumNursingMedical educationPsychologyMedicinePedagogy

Abstract

fetched live from OpenAlex

OBJECTIVE: This scoping review sought to establish the current state of knowledge regarding physical assessment skills taught globally in undergraduate nursing curricula. Explicitly, the review aimed to determine which skills are being taught via curricula and which skills are performed by students in clinical placements, as well as what physical assessment skills are being used by registered nurses in practice. INTRODUCTION: Nursing programs are expected to teach the physical assessment skills required for entry-level registered nurses to practice competently. The discrepancy lies in determining which skills are essential to teach entry-level nurses and which are unessential. INCLUSION CRITERIA: Studies that examined physical assessment skills taught to students in any undergraduate registered nursing program or used by registered nurses in practice were considered. Physical assessments included all techniques or skills taught in any year of a university or college teaching global registered nursing curricula. METHODS: Databases searched included MEDLINE (Ovid), CINAHL Complete (EBSCO), Scopus, and Cochrane Central Register of Controlled Trials (Ovid). Sources of unpublished studies included ProQuest Dissertations and Theses Global, OpenGrey, Open Access Theses and Dissertations, and Google Scholar. Studies published in English between January 2008 and November 2019 were included. Two independent reviewers screened titles and abstracts. Studies meeting the inclusion criteria were imported into the Covidence systematic review manager. Extracted data were presented in a descriptive format, including characteristics of included studies and relevant key findings. RESULTS: Thirteen records were extracted for synthesis: one integrated review, one author reflection, one mixed methods study, and 10 quantitative studies. The sources represented a global context: the United States, New Zealand, Turkey, Australia, Norway, Korea, Italy, and one of unknown origin. Three studies examined physical assessment skills routinely taught in global nursing curricula. Three studies explored physical assessment skills routinely used by students during nursing programs. Seven studies examined which physical assessment skills were routinely performed by registered nurses in practice. In the studies, there were 98 to 122 physical assessment skills taught in global nursing programs. However, only 33 skills were routinely taught in curricula, and of those, only 20 were the same across all studies (core skills). Students in nursing programs routinely performed 30 physical assessment skills, and six of the 30 skills were the same across all studies (core skills). Of the six core skills routinely performed by students, five were also routinely taught in nursing curricula in the included studies. Registered nurses routinely performed 39 physical assessment skills, and 11 skills were the same across all studies (core skills). Ten of the physical assessment skills taught in curricula were routinely performed by registered nurses in practice. CONCLUSION: This scoping review provides insight into physical assessment skills taught in nursing curricula and used by registered nurses in practice. This knowledge is essential for curriculum revisions and planning as it provides insight on how to best meet the needs of future nursing students.

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)0.038
metaresearch head score (Gemma)0.151
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.038
Threshold uncertainty score0.199

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0380.151
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0090.007
Bibliometrics0.0340.036
Science and technology studies0.0020.003
Scholarly communication0.0080.008
Open science0.0030.004
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.032
GPT teacher head0.456
Teacher spread0.424 · 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 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

Citations16
Published2021
Admission routes1
Has abstractyes

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