Bibliographic record
Abstract
I n marking the 10th anniversary of PEN: Practice-based Evidence in Nutrition®, I had the opportunity to reflect on Publishing Trends over 10 Years in the PEN® eNews 10th Anniversary Issue September 2015.I began this task by searching PubMed for "trends" in the Journal, which revealed an interesting array of papers covering nutrition assessment and counselling, food and cooking skills, population intake of processed foods, and environmental food services.For example, we now take dietitians in primary health care for granted, but 10 years ago, we were building this vision and the Journal supported an entire "Supplement on Primary Health Care".This publication helped to shape this important area for dietitians by providing thought leadership and offering opportunities to develop a community of practice.The other way to look at trends was to examine the articles most cited by other researchers as reported on Google Scholar.Over the last 10 years, the most cited articles from the Journal reflect the broad range of topics addressed in dietetic practice, including:• health conditions (e.g., cancer, inflammatory diseases) • population health (e.g., intake of processed foods) • lifecycle issues (e.g., school-aged children, at-risk youth, women's health issues) • food and nutrients (e.g., selenium, gluten-free foods, food skills) • professional practice (e.g., dietetic education, dietary assessment using mobile devices)In the next 10 years, I imagine we will continue to look more to technology and social media to support our varied practice settings.The Journal continues to be an important vehicle for thought leadership in our profession, supporting knowledge information and exchange.I feel privileged to be a part of the continually evolving field of nutrition and dietetic practice.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.640 | 0.724 |
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".