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Record W1948601862 · doi:10.22230/jripe.2014v4n1a172

Oxygen and Ventilator Treatment: Perspectives on Interprofessional Collaboration in a Neonatal Intensive Care Unit

2014· article· en· W1948601862 on OpenAlexvenueno aff
Marianne Trygg Solberg, Thor Willy Ruud Hansen, Ida Torunn Bjørk

Bibliographic record

VenueJournal of Research in Interprofessional Practice and Education · 2014
Typearticle
Languageen
FieldHealth Professions
TopicFamily and Patient Care in Intensive Care Units
Canadian institutionsnot available
Fundersnot available
KeywordsNorwegianNursingMedicineNeonatal intensive care unitFocus groupPsychological interventionNeonatal nursingQualitative researchMechanical ventilationFlexibility (engineering)PerceptionSick childIntensive care unitIntensive care medicinePsychologyPediatrics

Abstract

fetched live from OpenAlex

Background: The aim of this study was to explore perspectives on the collaboration between physicians and nurses managing oxygen and ventilator treatment of sick infants in a Norwegian neonatal intensive care unit.Methods and Findings: We performed a qualitative study using focus groups. We found that interprofessional collaboration concerning newborns on mechanical ventilation lacked co-ordination and was unsystematic. This led to inadequate utilization of the medical and clinical competency of the nursing staff. Nurses and physicians approached decision-making differently, and there was limited flexibility and dynamics in the allocation of responsibility between the professionals.Conclusion: Findings from this study indicate that nurses and physicians have the opportunity to improve the quality of care by developing high-quality communication, formulating plans together, and improving the co-ordination of the ventilator treatment. Further studies should develop and test interventions based on the professionals’ perception of relevant co-ordination strategies to improve mechanical ventilation and oxygen treatment to premature and sick newborn infants.

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)0.002
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.326
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
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 head0.106
GPT teacher head0.533
Teacher spread0.426 · 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 designQualitative
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

Citations6
Published2014
Admission routes1
Has abstractyes

Explore more

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