MétaCan
Menu
Back to cohort
Record W3083560519 · doi:10.21037/tp-20-60

Attitudes and concerns of neonatologists and nurses to family- integrated-care in neonatal intensive care units in China

2020· article· en· W3083560519 on OpenAlexafffund
Xiying Xiang, Shiwen Xia, Xing Zhu, Xiangyu Gao, Xirong Gao, Aimin Zhang, Shoo K. Lee, Mingyan Hei

Bibliographic record

VenueTranslational Pediatrics · 2020
Typearticle
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsUniversity of TorontoMount Sinai Hospital
FundersCanadian Institutes of Health Research
KeywordsMedicineNeonatal intensive care unitIntensive careChinaSurvey researchSession (web analytics)QuestionnaireFamily medicineNursingPediatricsPsychologyIntensive care medicine

Abstract

fetched live from OpenAlex

Background: Parents in China are denied visitation of their newborns in neonatal intensive care unit (NICU), leading to a prolonged period of parent-infant separation. The family-integrated care (FICare) model, which supports the integration of parents into the NICU team, is gradually being introduced in China. Considering resistance to the implementation of FICare, this study aimed to assess the attitudes and concerns of neonatologists and nurses towards FICare in China. Methods: Using a before and after study design, a qualitative analysis was conducted to determine the perceptions and attitudes of medical professionals towards FICare in China. A total of 34 neonatologists and 94 nurses from 5 tertiary NICUs in China were enrolled. A self-developed questionnaire was used. The study steps included reading session and then survey for the first time (survey 1), a FICare getting buy-in education session (4 hours), a group discussion session, and finally repeat the questionnaire (survey 2). The surveys were completed by trained researchers regarding willingness, acceptance and concerns of implementing FICare in NICUs in China. Differences in attitudes towards FICare were compared between groups (Chi-square/correction for continuity). Results: There are positive responses in neonatologists and nurses regarding the necessity (Survey 1: 58.8% and 57.4%; Survey 2: 88.2% and 67.0%), feasibility (Survey 1: 17.6% and 19.1%; Survey 2: 32.3% and 34.0%), and interest in joining FICare (Survey 1: 82.4% and 83.0%; Survey 2: 97.1% and 85.1%). A higher proportion of neonatologists indicated that FICare could promote breastfeeding in the NICU comparing to nurses (Survey 1: 47.1% vs. 19.1%; Survey 2: 61.8% and 46.8% respectively). Most of the neonatologists and nurses are not sure whether FICare can shorten the hospital stay (Survey 1: 82.3% and 68.1%; Survey 2: 85.3% and 60.6%) or improve the doctor-patient relationship (Survey 1: 58.8% and 68.1%; Survey 2: 73.5% and 69.1%). Challenges concerning the implementation of FICare were identified as inadequate ward space, lack of human resources, and potential increases in nosocomial infection. Conclusions: The getting buy-in education program in introducing new paradigms of neonatal care may help on how to design and implement more effective educational tools for FICare.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.289
Teacher spread0.263 · 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 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

Citations8
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueTranslational PediatricsSame topicInfant Development and Preterm CareFrench-language works237,207