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Effect of Nursing Education on Positioning of Infants in the Neonatal Intensive Care Unit

2004· article· en· W2082431745 on OpenAlexaff
Emmi Perkins, Lesley Ginn, Jamie Kneale Fanning, Doreen J. Bartlett

Bibliographic record

VenuePediatric Physical Therapy · 2004
Typearticle
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsWestern UniversitySt Joseph's Health Care
Fundersnot available
KeywordsNeonatal intensive care unitNursingContext (archaeology)PerceptionMedicinePosition (finance)PsychologyIntensive care unitNeonatal nursingIntensive carePediatricsIntensive care medicine

Abstract

fetched live from OpenAlex

PURPOSE: To determine the effect of different forms of education on nurses' abilities to position neonates in a developmentally supportive way and to determine nurses' perceptions of effectiveness of educational methods to enhance their positioning abilities. METHOD: In a one-group, repeated-measures, alternating-treatment design, the effectiveness of nurses' abilities to position neonates in the context of developmentally supportive care before and after different education approaches was scored using an instrument designed for this study. A self-administered survey was conducted with nurses to examine perceptions of positioning-related issues. RESULTS: Formal education methods such as in-services and workshops improved nurses' abilities to position neonates in developmentally supportive positions; however, improvements declined in the absence of ongoing education. Nurses perceived workshops, physical therapy in-services, and bedside consultation to be more useful than audiovisual resources, independent reading, or general hospital in-services. CONCLUSION: The results suggest that the physical therapist's role as a consultant is important to ensure continued performance of developmentally supportive care with respect to positioning of infants in the neonatal intensive care unit.

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)0.002
metaresearch head score (Gemma)0.018
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.018
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 science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.335
Teacher spread0.320 · 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

Citations14
Published2004
Admission routes1
Has abstractyes

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