Bibliographic record
Abstract
What can make your work as a dietitian so meaningful that you begin each day with enthusiasm, and if you so choose, retain that joy in your work for 5 decades or more? Three themes are: (i) doing work that profoundly makes sense to you, (ii) inspiring others (and yourself) to make healthful choices, and (iii) moving through challenges to success. Initially it can be challenging to make a living through work that is most deeply meaningful or closest to your heart. Yet it is well worth finding the balance between practicality and movement in the desired direction. Other challenges faced by dietitians involve helping others to adopt new, more healthful lifestyle choices. As health professionals, our attitudes towards plant-based diets have changed dramatically during these past decades. This article examines our evolving perspectives of plant-based diets, and uses this as an example of movement through challenges to success and acclaim. Vegetarian and vegan diets that were considered entirely inappropriate for many stages of the life cycle in the 1970s are now seen to confer health benefits. This applies to well-designed plant-based diets, thus offering a significant role for dietitians as creative leaders in this field.
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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.016 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.010 | 0.022 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".