Dietitians Working in Continuing Care Facilities in Nova Scotia: Pre- and Post-COVID-19 First Wave
Bibliographic record
Abstract
Continuing care (CC) facilities have been impacted by a growing demand for services, insufficient resources for the provision of quality food and nutrition care, and, most recently, the COVID-19 pandemic. This study explored the roles and responsibilities of dietitians working in CC facilities in Nova Scotia (NS) before and after the COVID-19 first wave. Using ethics-approved questionnaires, the estimated 75 dietitians working in CC facilities in NS were surveyed in Fall 2019 and Fall 2020 about their roles and responsibilities. Twenty responded to the first questionnaire and 15 to the second. Analysis of data included simple statistical and qualitative description methods. The findings highlighted the complexities and challenges faced by these dietitians in the provision of resident nutrition care, overseeing foodservices, training staff and dietetic interns, and contributing to facility specific care committees before and after the COVID-19 first wave. There is a need to advocate for minimum standards for dietetic and foodservice funding in CC facilities based on higher acuity and complex care needs of residents and considering the multifaceted roles of dietitians in CC. Efforts to improve awareness about the roles of dietitians working in CC among resident families, other dietitians, and dietetic interns are also needed.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".