The Nutrition and Dietetics Workforce Needs Skills and Expertise in the New York Metropolitan Area
Bibliographic record
Abstract
BACKGROUND: There is an increased demand in the Nutrition and Dietetics field which has fostered credentialing to ensure competent graduates. The objective of this study is to conduct an exploratory analysis to identify nutrition/dietetics workforce needs, skills and expertise in the New York metropolitan area as exemplified in position announcements over a 4 year period. METHODS: We recorded position announcements for jobs in nutrition and dietetics from the New York State Registered Dietitian Yahoo group, and the Hunter College Nutrition and Food Sciences student and alumni listserv (NFS-L) over a 4 year period. Keywords were identified using job categories defined by the Academy of Nutrition and Dietetics (AND) compensation and benefits survey. This served as a starting point to enumerate the types of positions that have been posted for the New York metropolitan area in recent years. RESULTS: Four hundred and twelve (412) unique job postings were recorded. Various educational levels, credentials, and skills desired by these employers were identified, assessed, and compared with similar data from the "supply side" reports from AND. CONCLUSIONS: The credentials and skills most desired by employers are similar to some of the learning objectives set forth for DPD and DI programs by ACEND, but not entirely congruent. The need for both client/customer focus and computer literacy may be implicit in the standards, but a more overt inclusion of these skills would likely be of benefit to ensure these are inculcated into every program and student.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".