Evaluation of Protein Cards: A nutrition education tool for metabolic bariatric surgery
Bibliographic record
Abstract
Abstract Background Metabolic bariatric surgery (MBS) is a safe, effective treatment for severe obesity and its associated comorbidities. However, adherence to postoperative guidelines, particularly dietary protein intake remains a challenge. This study examined the suitability of the Protein Cards , a protein-focused nutrition education tool developed to support individuals meet their protein requirements during the postoperative diet stages: fluid, purée, soft, and regular diets. Methods An online adapted version of the Suitability Assessment of Materials questionnaire was administered from September 2020 to May 2021. Participants were recruited via convenience sampling and advertisement. The tool was rated as “superior”, “adequate” or “not suitable” on content, literacy demand, graphic illustrations, layout and typography, learning stimulation and motivation, and cultural appropriateness, with scores of 2, 1, and 0, respectively. Results A total of 442 individuals completed the online survey. Participants were identified as individuals who have undergone MBS (n= 268), caregivers of individuals who completed MBS (n =68), and/or healthcare providers specializing in MBS (n =106). The Protein Cards received an overall “superior” rating of 73.16%. The tool had a high likelihood of use particularly for the soft diet stage (63.98 ± 20.71). Participants preferred the tool be available as a mobile application (63.46 ± 20.44) followed by paper book (59.42 ± 22.12) format. Conclusion The development of nutrition education tools is essential for supporting individuals who have undergone MBS in adopting healthy dietary habits, particularly in their meeting protein requirements. Future studies will refine the Protein Cards and evaluate its usability among individuals post-MBS.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".