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Record W3032622478 · doi:10.3148/cjdpr-2020-012

Caregivers’ Perceptions of Real-Food Containing Tube Feeding: A Canadian Survey

2020· article· en· W3032622478 on OpenAlexaffvenueabout
Michelle Boston, Heather Wile

Bibliographic record

VenueCanadian Journal of Dietetic Practice and Research · 2020
Typearticle
Languageen
FieldMedicine
TopicChild Nutrition and Feeding Issues
Canadian institutionsNestlé (Canada)Science North
Fundersnot available
KeywordsConsistency (knowledge bases)PerceptionFeeding tubeMedicineFamily medicinePsychologyMathematicsSurgery

Abstract

fetched live from OpenAlex

Caregivers of children requiring tube feeding show growing interest in real-food containing formula, including home-blenderized tube feeding (HBTF) and commercial real-food containing formulas (CRFCF). This study aimed to understand caregivers' perceptions of both. Caregivers using real-food containing tube feeding were recruited through the Feeding Tube Awareness Foundation Facebook group. A 13-question online survey asked about use of HBTF and CRFCF, beliefs about their choices, and what resources guided formula use. Forty-one completed the survey, with mean child age of 7 years. Overall, 54% (n = 22) used HBTF formulas, 34% (n = 14) CRFCF, and 12% (n = 5) used both. For 70% (n = 29), presence of whole foods, nutritional completeness, and natural ingredients were most important. Challenges with CRFCF use included lack of variety (n = 10, 53%) and cost (n = 9, 47%). HBTF challenges were difficulty preparing away from home (n = 19, 70%) and need for special blenders (n = 15, 56%). Participants believed CRFCF are convenient (n = 35, 85%) and nutritionally consistent (n = 25, 61%), but do not contain enough real-food ingredients (n = 26, 63%). Facebook or other social media was the most valued resource guiding formula use (n = 25, 61%). Caregivers desire formulas that are nutritionally complete and made of whole foods. CRFCF offers convenience and consistency, yet caregivers prefer more real-food ingredients.

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)0.008
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 score0.127
Threshold uncertainty score0.971

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.164
GPT teacher head0.396
Teacher spread0.232 · 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

Citations14
Published2020
Admission routes3
Has abstractyes

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