Optimizing Nutrition Care in Hospital: Understanding Current Nutrition Practices through Focus Groups and Interviews
Bibliographic record
Abstract
The More‐2‐Eat (M2E) project aims to optimize nutrition care in Canadian hospitals through use of the Integrated Nutrition Pathway for Acute Care (INPAC). By optimizing nutrition care, the M2E project aims to impact performance of the healthcare system by ensuring that malnutrition and poor food intake are prevented, detected and treated, hence promoting the recovery, function and quality of life of patients, with particular attention on the needs of frail elderly. Before making a change to nutrition culture in hospital, it is important to understand current attitudes and practices related to nutrition care from a variety of hospital staff and management perspectives. As part of baseline data collection for M2E, interviews (n=40) and focus groups (n=11) were conducted with staff and management from 5 Canadian hospitals, in 4 provinces. Thematic analysis of transcripts is currently underway and preliminary findings identify facilitators and barriers regarding incorporation of nutrition screening tools, provision of standard nutrition care, use of nutrition assessment tools, incorporation of nutrition into the discharge process, and suggestions for making change in the hospital. Initial results indicate that communication is a problem across departments. Although communication between individuals is seen as strong, it is not always clear who is responsible for certain tasks, there are often several steps required to communicate a patient need/care, and it is not always clear how to communicate patient needs to effect change in behaviour of staff. There is also a lack of ownership regarding mealtimes, making it more difficult to make a change if no one is accountable. The staff care about nutrition and their patients, however need specific and manageable tasks which can be built into their current workload. Change should begin small and be tested for feasibility before upscaling. Incorporating all staff will be essential for this culture change. The results of the focus groups and interviews will be summarized for each unit and presented back to them as a starting point, along with quantitative data on their current processes. Feedback of this data will be used to stimulate change in nutrition care on the unit. Understanding the facilitators, barriers and opportunities to change are essential when optimizing nutrition care in hospital. Support or Funding Information This research is funded by the Technology Evaluation in the Elderly Network (TVN), which is supported by the Government of Canada through the Networks of Centres of Excellence program.
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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 | 0.032 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".