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Record W3003254109 · doi:10.1111/mcn.12956

A measurement scale to assess responsive feeding among Cambodian young children

2020· article· en· W3003254109 on OpenAlexaff
Ndèye S. Sall, France Bégin, Jérémie B. Dupuis, Jimmy Bourque, Lylia Menasria, Barbara Main, Lenin Vong, Vannary Hun, David Raminashvili, Chea Chhorvann, Lucie Chiasson, Sonia Blaney

Bibliographic record

VenueMaternal and Child Nutrition · 2020
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsCanadian Celiac AssociationCollège Communautaire du Nouveau-BrunswickRoyal College of Physicians and Surgeons of CanadaUniversité de Moncton
Fundersnot available
KeywordsMedicineMalnutritionScale (ratio)Baseline (sea)Reliability (semiconductor)Confirmatory factor analysisPromotion (chess)Intervention (counseling)Environmental healthNutrition EducationGerontologyStructural equation modelingStatisticsNursing

Abstract

fetched live from OpenAlex

The caregiver-child interaction during mealtime, which refers to responsive feeding (RF), influences child's dietary intake. In Cambodia, given the level of malnutrition, getting better knowledge of RF among young children is essential, but to do so, using an appropriate assessment tool is necessary. We aim to develop and to validate a measurement tool to assess RF in two different situations (before and after an intervention) among children 6-23 months old. This research is part of a larger trial assessing the impact of nutrition education combined or not with the provision of complementary foods on child nutritional status. The "Opportunistic Observation Form" from the Process for the Promotion of Child Feeding package was used to collect data on RF through direct observations of child's meal episodes. Data were used to define an initial scale composed of four constructs and 15 indicators. Confirmatory factor analyses (CFA) and Hancock and Mueller's H reliability indices were computed to assess the validity and reliability of the scale. The final tool was applied to baseline and endline data. At baseline, the sample included 243 pairs and, at endline, 248 pairs. The final scale included two latent constructs (RF and active feeding) that comprise three indicators for active feeding and five for RF. Criteria for fit indices of CFA were met for both constructs though better at baseline. Reliability coefficients were above 0.80 for each construct at baseline and endline. This research proposes a scale that could be used to assess active feeding and RF. Further validation is warranted in different contexts.

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)0.000
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 score0.091
Threshold uncertainty score0.867

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.023
GPT teacher head0.242
Teacher spread0.219 · 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

Citations21
Published2020
Admission routes1
Has abstractyes

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