Learning to lead: A pilot study on dietitians' reflections on critical experiences that required leadership
Bibliographic record
Abstract
Introduction: Patients with chronic kidney disease (CKD) should limit dietary intake of sodium (Na), phosphorus (P), and potassium (K) as high intakes are associated with increased morbidity. These minerals are frequently added to soup as food additives. Although their presence is indicated in the ingredient list, P and K content may not always be present on the Nutrition Facts table (NFt), making it difficult for patients to choose appropriate foods. Objectives: 1)Examine the impact of additives on the amounts of Na, P and K indicated on NFt of commercial soup products. 2)Compare the chemically analyzed K content in soups with and without K-additives. Methods: From an inclusive convenience sample, data from ingredient lists and NFt were collected from canned and boxed soups, both generic and brand name, at three major grocery stores in Ottawa (n=126). A subset of soups with K-additives (n=11), matched with similar soup types without K-additives (n=11), were analyzed for K content by AOAC official method.Results: Soups with Na-additives (95%) had significantly more Na indicated on NFt than soups without Na-additives (661u00b1173 vs 41u00b124 mg/g, p <0.001). Soups with P-additives (29%) had no P content on NFt. Half of soups with K-additives (27%) listed K content on NFt, which was similar to soups without K-additives. However, chemically analyzed soups with K-additives had significantly more K vs those without K-additives (641u00b174 vs. 269u00b135 mg/g, p<0.001). Conclusion: Soups with Na-additives may have sixteen times more Na than soups without. K content of soups may be high and cannot be inferred from the presence of K-additives on product label. Significance to the Field of Dietetics: Patients with CKD should be wary of consuming commercial soups given high Na content, and frequently missing K and P content on NFt. Findings support the eventual inclusion of K content on the NFt.
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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 | 0.018 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".