Implementing best practice in hospital multidisciplinary nutritional care: an example of using the knowledge-to-action process for a research program
Bibliographic record
Abstract
BACKGROUND: Prospective use of knowledge translation and implementation science frameworks can increase the likelihood of meaningful improvements in health care practices. An example of this creation and application of knowledge is the series of studies conducted by and with the Canadian Malnutrition Task Force (CMTF). Following a cohort study and synthesis of evidence regarding best practice for identification, treatment, and prevention of malnutrition in hospitals, CMTF created an evidence-informed, consensus-based pathway for nutritional care in hospitals. The purpose of this paper is to detail the steps taken in this research program, through four studies, as an example of the knowledge-to-action (KTA) process. THE KTA PROCESS: The KTA process includes knowledge creation and action cycles. The steps of the action cycle within this program of research are iterative, and up to this point have been informed by three studies, with a fourth underway. The first study identified the magnitude of the malnutrition problem upon admission to hospital and how it is undetected and undertreated (study 1). Knowledge creation resulted in an evidence-based pathway established to address care gaps (study 2) and the development of monitoring tools (study 3). The study was then adapted to local context: focus groups validated face validate the evidence-based pathway; during the final phase, study site implementation teams will continue to adapt the pathway (studies 2 and 4). Barriers to implementation were also assessed; focus groups and interviews were conducted to inform the pathway implementation (studies 1, 2, and 4). In the next step, specific interventions were selected, tailored, and implemented. In the final study in this research program, plan-do-study-act cycles will be used to make changes and to implement the pathway (study 4). To monitor knowledge use and to evaluate outcomes, audits, staff surveys, patient outcomes, etc will be used to record process evaluations (studies 3 and 4). Finally, a sustainability plan will be incorporated into the final study of the program (study 4) to sustain knowledge use. DISCUSSION: Use of frameworks can increase the likelihood of meaningful and sustainable improvements in health care practice. The example of this program of research demonstrates how existing evidence has been used to identify, create, and adapt knowledge, and how multidisciplinary teams have been used to effect changes in the hospital setting. CONCLUSION: Effective implementation is essential in nutritional health care, and this multidisciplinary program of research provides an example of how the KTA process can facilitate implementation and promote sustainability.
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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 | 0.417 | 0.274 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.030 | 0.065 |
| Scholarly communication | 0.027 | 0.022 |
| Open science | 0.009 | 0.035 |
| Research integrity | 0.014 | 0.021 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".