Bibliographic record
Abstract
The Sinai Health System comprises of two hospitals including Bridgepoint Active Healthcare, a rehabilitation and complex care hospital. Here, I worked as a dietetic student in inpatient medical rehab, transitional care, renal (kidney) and palliative care units for 8-weeks. In addition, I was placed at Bridgepoint Family Health Team (FHT) for 4-weeks, where I worked with the Diabetes Education Program (DEP) team, which consisted of a registered dietitian and advanced practice nurse. At the hospital, we supported patients to adequately meet their nutrition needs throughout rehabilitation and improve their overall nutritional status for a smooth transition back into community after discharge. By providing nutritional support and nutrition education, patients may be better equipped to take care of their health leading to better possible health outcomes, a reduced number of future hospital visits and therefore a reduced burden on the healthcare system. Additionally, we helped complex care patients to maintain their nutritional status during their long-term stays. Of particular mention are patients in the renal unit living with diabetes as it connected to my FHT rotation. At the FHT, the DEPT team saw adults at risk, recently diagnosed or living with diabetes to help prevent or manage their condition through diet counselling, education and encouraging healthy behaviours. The goal was to help prevent further complications associated with poorly managed diabetes, such as renal disease. This, represents the point of intersection of clinical practice and public health, where the bridge between preventative and curative healthcare can be observed. Ultimately, nutrition care throughout the various stages of the healthcare journey is important to public health and its mission - to decrease the prevalence of chronic disease, such as diabetes, and maintain and promote healthier communities through effective healthcare.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".