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Record W1559188345 · doi:10.3233/wor-2010-1090

Nurse perceptions of manual patient transfer training: Implications for injury

2010· article· en· W1559188345 on OpenAlexaff
Paula M. van Wyk, David M. Andrews, Patricia L. Weir

Bibliographic record

VenueWork · 2010
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of WindsorWestern University
Fundersnot available
KeywordsNursingParticipatory ergonomicsPerceptionMedicinePsychologyMEDLINEAcademic institutionJob satisfactionMusculoskeletal injuryMedical educationHuman factors and ergonomicsFamily medicinePoison controlMedical emergencyAlternative medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: The purpose of this study was to evaluate the perceptions of student and staff nurses regarding training they received and their confidence in performing a variety of common manual patient transfers (MPTs), given that inadequate training may have implications for injury risk. PARTICIPANTS: Student nurses (n=163) from a mid-sized university and staff nurses (n=33) from a small rural hospital in the university's region. METHODS: Participants were surveyed to determine which of 19 MPTs they perceived having received training for and had greatest confidence performing. RESULTS: The staff nurses perceived being trained on four MPTs; the same four they indicated they had the greatest confidence performing. However, nursing students were not trained on these MPTs at the local university, indicating an apparent disconnect in training practices between the academic institution and the workplace. CONCLUSIONS: It is suggested that a participatory ergonomics training approach may help to provide student nurses more opportunity to practice MPTs and help all nurses reduce work-related musculoskeletal injury risk and increase job satisfaction. Increased training time may also allow student nurses to gain greater mastery and confidence of skills prior to full-time employment.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.938
Threshold uncertainty score0.204

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.318
Teacher spread0.304 · 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.

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

Citations7
Published2010
Admission routes1
Has abstractyes

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