Navigating the nutrition information landscape for healthcare providers of residents in long-term care homes at the end of life
Bibliographic record
Abstract
Introduction: End of life (EOL) care is provided to residents in long-term care (LTC) homes and aims to support quality of life until death. EOL care does not always meet the expectations of caregivers as they often receive inconsistent and inadequate information about the resident’s care at EOL. Issues of short-staffing and lack of EOL nutritional practices in LTC have been further accentuated by COVID-19. Objectives: The objectives of this study were to examine 1) How healthcare providers (HCPs) perceive food and eating at the end of life for older adults in LTC in normal conditions and during a pandemic; and 2) What EOL nutrition conversations in LTC currently look like. Methods: Sixteen HCPs working in Manitoba LTC homes were recruited and interviewed [female (88%), mean age ± SD = 42 ± 10.2 years; dietitians (62.5%)]. The semi-structured interviews were audio-recorded, transcribed verbatim, and analyzed using inductive content analysis. Results: Three themes emerged from the interviews. 1) Negotiating tensions in nutrition at EOL: tensions arise from differing expectations and the importance of nutrition of the HCPs, caregivers, and sometimes the residents. HCPs provide education to caregivers on the role of food at EOL and what is best for the comfort of the resident. 2) Bridging the nutritional divide: EOL nutrition information is provided by HCPs to caregivers mostly through in-person conversations. HCPs stated that the most appropriate time to initiate these conversations is at admission or when there is a change in the resident’s condition. 3) Challenges during COVID-19: visitor restrictions limited conversations to be over the phone and HCPs had to provide more frequent updates to bridge this gap. Due to the rapid progression of the virus, conversations about the nutritional care of COVID-19-positive residents involved more discussion of management of gastrointestinal issues and predicting health trajectory. Conclusion: HCPs in LTC navigate tensions at EOL through balancing comfort and nutritional needs of the resident, and bridge the nutritional divide by providing education to caregivers. The COVID-19 pandemic affected the way EOL nutritional conversations were carried out due to visitor restrictions and the rapid progression of the virus.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".