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Record W3150948526 · doi:10.1111/1747-0080.12663

<scp>Systematised, Interdisciplinary Malnutrition Program for impLementation and Evaluation</scp> delivers improved hospital nutrition care processes and patient reported experiences – An implementation study

2021· article· en· W3150948526 on OpenAlexaff
Jack Bell, Adrienne Young, J. Hill, Merrilyn Banks, Tracy Comans, Rhiannon Barnes, Heather Keller

Bibliographic record

VenueNutrition & Dietetics · 2021
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsResearch Institute for AgingUniversity of Waterloo
Fundersnot available
KeywordsMalnutritionMedicineClinical nutritionAuditMedical nutrition therapyParenteral nutritionIntervention (counseling)NursingFamily medicineIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

AIM: Models of hospital malnutrition care reliant on dietitians can be inefficient and of limited effectiveness. This study evaluated whether implementing the Systematised, Interdisciplinary Malnutrition Program for impLementation and Evaluation (SIMPLE) improved hospital nutrition care processes and patientreported experiences compared with traditional practice. METHODS: A multi-site (five hospitals) prospective, pre-post study evaluated the facilitated implementation of SIMPLE, a malnutrition care pathway promoting proactive nutrition support delivered from time of malnutrition screening by the interdisciplinary team, without need for prior dietetic assessment. Implementation was tailored to local site needs and resources. Nutrition care processes delivered to inpatients who were malnourished or at-risk of malnutrition were identified across diagnosis, intervention, and monitoring domains using standardised audits from medical records, foodservice systems and patient-reported nutrition experience measures. RESULTS: Pre-implementation (n = 365) and post-implementation (n = 397) cohorts were similar for age (74 vs 73 years), gender (47.1% vs 48.6% female), and nutrition risk status (46.6% vs 45.3% at-risk). Post-implementation, at-risk participants were more likely to receive enhanced food and fluids (68.5% vs 83.9%; P < .01), nutrition information (30.9% vs 47.2%; P < .01), mealtime assistance where required (61.4% vs 77.9% P = .04), nutrition monitoring (25.2% vs 46.3%; P < .01) and care planning (17.8% vs 27.7%; P = .01). Patient-reported nutrition experience measures confirmed improved nutrition care. There was no difference in dietetic occasions of service per patient (1.51 vs 1.25; P = .83). CONCLUSIONS: Tailored SIMPLE implementation improves nutrition care processes and patient reported nutrition experience measures for at-risk inpatients within existing dietetic resources.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.010
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.046
GPT teacher head0.429
Teacher spread0.382 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations34
Published2021
Admission routes1
Has abstractyes

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