Caregivers’ Perceptions of Real-Food Containing Tube Feeding: A Canadian Survey
Bibliographic record
Abstract
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.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".