Evaluating the Impact of COVID19 on Nutrition Jobs in the New York Metropolitan Area: A Comparison of Position Listings and Reported Needs from Before and During the Pandemic
Bibliographic record
Abstract
The Covid19 pandemic has caused significant changes in staffing and training needs in the healthcare workforce. Among the practitioners impacted are dietitians and nutritionists. This study compared samples of position announcements in nutrition and dietetics from 2017 and 2021. Differences by both total number of position and position categories were found to be significantly different (p<.05) between the two time periods studied. There was a decrease of about 50% in the total number of positions posted in the first three months of 2021 as compared to the same months in 2017. Changes across position location were also significant. As a % of the total positions, specialty programs were the only category to show growth over the time period studied. These included overall increases in outpatient clinical programs focusing on HIV/AIDs, cancer, and hemodialysis, and more notably in 2021, eating disorders, obesity, and mental health programs. Differences in specific skills and credentials desired were consistent with needs in these practice areas. Awareness of changes in the employment landscape can help to better prepare students and interns to meet emerging patient care needs and workforce demands.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".