MétaCan
Menu
Back to cohort
Record W3206925410 · doi:10.1038/s41372-021-01220-5

The culture of research communication in neonatal intensive care units: key stakeholder perspectives

2021· article· en· W3206925410 on OpenAlexaff
Jennifer Degl, Ronald L. Ariagno, Judy L. Aschner, Sandra Sundquist Beauman, Wakako Eklund, Elissa Z. Faro, Hiroko Iwami, Yamile C. Jackson, Carole Kenner, Ivone Kim, Ágnes Klein, Mary B. Short, Keira Sorrells, M. Turner, Robert S. Ward, Scott K. Winiecki, Christina Bucci‐Rechtweg

Bibliographic record

VenueJournal of Perinatology · 2021
Typearticle
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsHealth Canada
FundersNational Association of Neonatal NursesChildren's Hospital of PhiladelphiaU.S. Department of Health and Human Services
KeywordsMedicineKey (lock)Intensive careNeonatologyStakeholderNeonatal intensive care unitIntensive care medicinePediatricsFamily medicinePublic relationsPregnancy

Abstract

fetched live from OpenAlex

OBJECTIVE: To assess the perspectives of neonatologists, neonatal nurses, and parents on research-related education and communication practices in the neonatal intensive care unit (NICU). STUDY DESIGN: Questionnaire circulated through interest groups and administered using the internet. RESULTS: 323 respondents responded to the survey. 52 were neonatologists, 188 were neonatal nurses, and 83 were parents of NICU graduates. Analysis was descriptive. Differences were noted between stakeholder groups with respect to whether current medications meet the needs of sick neonates, research as central to the mission of the NICU, availability of appropriate education/training for all members of the research team, and adequacy of information provided to parents before, during, and after a research study is completed. CONCLUSION: Engagement of nurses and parents at all stages of NICU research is currently suboptimal; relevant good practices, including education, should be shared among neonatal units.

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)0.003
metaresearch head score (Gemma)0.012
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 score0.094
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.329
GPT teacher head0.559
Teacher spread0.230 · 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

Citations17
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueJournal of PerinatologySame topicHealth Sciences Research and EducationFrench-language works237,207