Navigating Mealtimes to Meet Public Health Mandates in Long-Term Care During COVID-19: Staff Perspectives
Bibliographic record
Abstract
Context: Mealtimes in long-term care (LTC) settings play a pivotal role in the daily lives of residents. The COVID-19 pandemic and the required precautionary infection control mandates influenced many aspects of resident care within LTC homes, including mealtimes. Limited research has been conducted on how mealtimes in LTC were affected during the pandemic from staff perspectives. Objective: To understand the experiences of LTC staff on providing mealtimes during the pandemic. Methods: Semi-structured telephone interviews were conducted with 22 staff involved with mealtimes between February and April 2021. Transcripts were analysed using interpretive description. Findings: Three themes emerged from the analysis: (1) recognizing the influence of homes’ contextual factors. Home size, availability of resources, staffing levels and resident care needs influenced mealtime practices during the pandemic; (2) perceiving a compromised mealtime experience for residents and staff. Staff were frustrated and described residents as being dissatisfied with mealtime and pandemic-initiated practices as they were task-focused and socially isolating and (3) prioritizing mealtimes while trying to stay afloat. An ‘all hands-on deck’ approach, maintaining connections and being adaptive were strategies identified to mitigate the negative impact of the mandates on mealtimes during the pandemic. Limitations: Perspectives were primarily from nutrition and food service personnel. Implications: Overly restrictive public health measures resulted in mealtime practices that prioritized tasks and safety over residents’ quality of life. Learning from this pandemic experience, homes can protect the relational mealtime experience for residents by fostering teamwork, open and frequent communication and being flexible and adaptive.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 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".