Long-Term Care Registered Dietitians’ Initial Response to the COVID-19 Pandemic
Bibliographic record
Abstract
Abstract At the outset of the global pandemic, long-term care (LTC) homes in Canada were captured in media reports as the centre of Canada’s COVID-19 epidemic. An estimated 80% of all COVID-19 deaths in Canada were associated with LTC outbreaks as of May 25, 2020. Infection control measures have swiftly changed the environment in many LTC homes for residents, workers, loved ones, and other supports. Registered Dietitians (RDs) are among the many care professionals working in LTC affected by these changes. The aim of this qualitative study was to examine the roles of RDs in supporting LTC residents during the initial phases of the pandemic. RDs faced remote practice, redeployment to address pandemic priorities, or cohorting to a sole practice site, yet were responsible for resident nutritional health. In-depth, web-based, semi-structured interviews with thirteen RDs working in LTC in a prairie province of Canada were used to explore the changes to work, challenges faced, impact on residents, and innovations in practice. The findings from this study capture nutrition and wellness-related implications of the COVID-19 pandemic within LTC homes. Examining the initial response of LTC RDs to the COVID-19 pandemic can help in planning for opportunities to support or enhance delivery of nutrition care in LTC homes, both in the context of the ongoing pandemic as well as future practice.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".