Coming Full Circle: Reflections on a Career as a Dietitian
Bibliographic record
Abstract
The connections between people, careers, events, and decisions provide the focus for this lecture, which traces the story of a 38-year career as a dietitian, spanning the country from east to west, through the centre, and back again. The lecture emphasizes the importance of taking inspiration from family and events, developing and maintaining lifelong friendships, and commitments. Finding opportunity in the midst of adversity is also a theme. The author's career begins with a clinical and administrative experience and moves into the community when she becomes a public health nutritionist. While the budget restrictions of the 1990s were challenging, the author recounts opportunities with key issues such as folic acid, prenatal nutrition, and heart health. A provincial food and nutrition plan was created, including a focus on food security and its connection to poverty. This is linked to the roots of the dietetic profession with the Lillian Massey School of Household Science and Art established in the 1890s. As the author recounts her journey, dietitians are reminded that working with partners and other disciplines provides the foundation for success and will be needed as we address the current issue of obesity.
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 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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.038 | 0.015 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.010 | 0.031 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".