Impact of Facilitated Behavior Change Strategies on Food Intake Monitoring and Body Weight Measurements in Acute Care: Case Examples From the More‐2‐Eat Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Assessing and monitoring food intake and body weight of all hospital patients is considered part of "best practice" nutrition care. This study presents case examples describing the impact of behavior change strategies on embedding these 2 monitoring processes in hospitals. METHODS: Four hospital medical units that participated in the More-2-Eat implementation study to improve nutrition care focused on improving food intake and/or weight monitoring practices. The percentage of admitted patients who received these care practices were tracked through chart audits over 18 months. Implementation progress and behavior change strategies were documented through interviews, focus groups, scorecards, and monthly telephone calls. Case examples are explored using mixed methods. RESULTS: Of the 4 units, 3 implemented food intake monitoring. One provided food service workers the opportunity to record food intake, with low intake discussed by an interdisciplinary team during bedside rounds (increased from 0% to 97%). Another went from 0% to 61% of patients monitored by introducing a new form ("environmental restructuring") reminding staff to ask patients about low intake. A third unit increased motivation to improve documentation of low intake and improved from 3% to 95%. Two units focused on regularity of body weight measurement. One unit encouraged a team approach and introduced 2 weigh days/week (improved from 14% to 63%), while another increased opportunity by having all patients weighed on Saturdays (improved from 11% to 49%). CONCLUSION: Difficult-to-change nutrition care practices can be implemented using diverse and ongoing behavior change strategies, staff input, a champion, and an interdisciplinary team.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it