Families’ perception of proposed nutrition screening on admission to pediatric hospitals: a qualitative analysis
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".