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Record W4389398862 · doi:10.1080/20473869.2023.2269321

Experiences of feeding young children with Down syndrome: parents’ and health professionals’ perspectives

2023· article· en· W4389398862 on OpenAlexfundno aff
Silvana E. Mengoni, Bobbie Smith, Helena Wythe, Samantha Rogers

Bibliographic record

VenueInternational Journal of Developmental Disabilities · 2023
Typearticle
Languageen
FieldMedicine
TopicChild Nutrition and Feeding Issues
Canadian institutionsnot available
FundersUniversity of HertfordshireDown Syndrome Research Foundation
KeywordsPsychologyDown syndromeHealth professionalsDevelopmental psychologyHealth carePsychiatry

Abstract

fetched live from OpenAlex

Background: Children with Down syndrome are commonly reported to experience feeding problems in the early years. This study aimed to explore and synthesise the experiences of feeding young children with Down syndrome from parents and professionals, and the support needed and received during this time. Methods: Eight mothers and twelve healthcare professionals took part in semi-structured interviews. All participants had, or had supported, a child(ren) with Down syndrome aged 0-5 years. Results: Reflexive thematic analysis resulted in two themes and seven subthemes. Mothers had clear feeding goals and adapted their journeys to meet their child's individual needs, with support from professionals and peers. Professionals could empower parents by building confidence and offering proactive support, although a lack of knowledge about Down syndrome and difficulties accessing support undermined mothers' confidence in services. Conclusions: Breastfeeding and family mealtimes held significant value to mothers, and specialist and trusted support may be needed to help families achieve these goals.

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)0.007
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 score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.003
Scholarly communication0.0030.002
Open science0.0000.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.029
GPT teacher head0.332
Teacher spread0.303 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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