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Record W4396524920 · doi:10.1177/2333794x241248982

Factors Influencing the Implementation of Infant Warming Devices Among Healthcare Workers in Malawian Hospitals

2024· article· en· W4396524920 on OpenAlexafffund
Alinane Linda Nyondo‐Mipando, Mai‐Lei Woo Kinshella, Sangwani Salimu, Brandina Chiwaya, Felix Chikoti, Lusungu Chirambo, Ephrida Mwaungulu, Mwai Banda, Tamanda Hiwa, Marianne Vidler, Elizabeth Molyneux, Queen Dube, Joseph Mfutso‐Bengo, David A. Goldfarb, Kondwani Kawaza

Bibliographic record

VenueGlobal Pediatric Health · 2024
Typearticle
Languageen
FieldMedicine
TopicNeonatal Respiratory Health Research
Canadian institutionsBC Children's HospitalChildren's & Women's Health Centre of British ColumbiaUniversity of British Columbia
FundersCanadian Institutes of Health ResearchGlobal Affairs CanadaInternational Development Research Centre
KeywordsMedicineHypothermiaHealth careEnvironmental healthMedical emergencyEconomic growthAnesthesia

Abstract

fetched live from OpenAlex

Objectives. Preterm infants are at risk of hypothermia. This study described the available infant warming devices (IWDs) and explored the barriers and facilitators to their implementation in neonates in Malawi. Methods. A qualitative descriptive study was conducted among 19 health care workers in Malawi from January to March 2020. All interviews were digitally recorded, transcribed, and managed using NVivo and analyzed using a thematic approach. Results. The warming devices included radiant warmers, Blantyre hot-cots, wall-mounted heaters, portable warmers, and incubators. Inadequate equipment and infrastructure and gaps in staff knowledge and capacity were reported as the main challenges to optimal IWD implementation. Caregiver acceptance was described as the main facilitator. Strategies to optimize implementation of IWD included continuous practical training and adequate availability of equipment and spare parts. Conclusion. Implementation of warming devices for the management of neonatal hypothermia is effective when there are adequate human and material resources.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.946

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.041
GPT teacher head0.425
Teacher spread0.384 · 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

Citations2
Published2024
Admission routes2
Has abstractyes

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