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Record W4387769791 · doi:10.1139/apnm-2023-0132

Families’ perception of proposed nutrition screening on admission to pediatric hospitals: a qualitative analysis

2023· article· en· W4387769791 on OpenAlexaffvenueabout
Sarah Kocel, Laura Carter, Marlis Atkins

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2023
Typearticle
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsAlberta Health Services
Fundersnot available
KeywordsPerceptionQualitative researchQualitative analysisMedicineFamily medicinePsychologySociology

Abstract

fetched live from OpenAlex

Nutrition screening is the first step in most acute care pediatric nutrition care pathways. However, there is a lack of understanding of patient and families' perception of nutrition screening in pediatric populations. The objective of this study was to explore the potential perceptions, feelings, and opinions of families if pediatric nutrition screening were to be completed during hospital admission. Nine members of the Family Advisory Council at the Alberta Children's Hospital participated in a focus group to discuss questions around nutrition screening practices, malnutrition, and the pediatric nutrition screening tool. Transcripts were analyzed using MAXQDA and thematic analysis using the Braun and Clarke methodology. Two major themes emerged: screening may raise sensitive emotions and understanding the purpose of nutrition screening and the questions. Participants agreed discussions around growth and nutrition are vital to comprehensive medical care; however, the timing and approach of nutrition screening can lead to anxiety and feelings of judgement. A lack of understanding of the purpose of screening, next steps, and benefit to the individual patient could limit acceptance of nutrition screening. The findings of this study can inform training and education of healthcare professionals involved in nutrition screening.

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)0.014
metaresearch head score (Gemma)0.026
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0050.004
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.059
GPT teacher head0.448
Teacher spread0.389 · 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

Citations2
Published2023
Admission routes3
Has abstractyes

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