More-2-Eat: evaluation protocol of a multi-site implementation of the Integrated Nutrition Pathway for Acute Care
Bibliographic record
Abstract
Nutrition care in hospitals is often haphazard, and malnourished patients are not always readily identified and do not receive the care they require. The Integrated Nutrition Pathway for Acute Care (INPAC) is an algorithm designed to improve the prevention, detection and treatment of malnutrition in medical and surgical patients. More-2-Eat is an evaluation of the implementation of INPAC care activities (e.g. screening) in five diverse medical units from different hospitals in Canada. The primary purpose is to understand how tailored implementation affects INPAC uptake and factors that impact this implementation. The principal outcome is a toolkit that can provide guidance to others. This participatory action research uses a before-after time series design to address several research questions focused on implementation and uptake of INPAC (e.g., Does the implementation of INPAC improve the detection of malnutrition? Do nutrition care related knowledge, attitudes and practices scores of unit staff change with the implementation of INPAC?). A six-month developmental phase where baseline data were collected is followed by a twelve-month implementation phase and a three-month sustainability phase. Qualitative and quantitative data are collected concurrently, and to address key research questions, these data are merged. Quantitative data are collected on-site by trained local dietitians and include chart audits of nutrition care practices and a more detailed assessment of recruited patients on quality of life, disability, frailty, food intake and barriers to food intake. Thirty-day post discharge follow up for these patients occurs by researchers via a telephone interview at three time points within baseline and implementation phases, to ascertain the same and other outcomes (e.g. readmission to hospital). Qualitative data include focus groups and key informant interviews completed by researchers, monthly teleconferences among the sites and site-completed forms that track implementation activities. Resource utilization of dietitian time for various care activities (e.g. assessment) and staff time to assist patients at mealtimes is also collected. More-2-Eat provides an example of how implementation can be tailored when a care algorithm is embedded into routine practice. The project also highlights important learning points with respect to data collection and techniques to support implementation. Retrospectively registered ClinTrials.gov Identifier: NCT02800304 June 7, 2016.
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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 | 0.124 | 0.085 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.064 | 0.013 |
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".