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1 Putting research into practice: knowledge translation and implementation for action on nutrition

2022· article· en· W4226071734 on OpenAlexaboutno aff
Jack Bell, Ellen Fallows, Peter Van Dael, Shane McAuliffe, Martin Kohlmeier, Alfredo Martínez Hernandez, Melissa Adamski, Sumantra Ray, Dominic Crocombe, Marjorie Lima do Vale

Bibliographic record

VenueOral Presentations · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNutrition, Genetics, and Disease
Canadian institutionsnot available
Fundersnot available
KeywordsSummitMedicinePopulationHealth carePandemicMedical educationNursingFamily medicinePsychologyCoronavirus disease 2019 (COVID-19)Environmental healthPolitical science

Abstract

fetched live from OpenAlex

The transfer of research evidence into practice has been historically slow, and requires an integration of many elements, including quality evidence, supportive physical and intellectual environments, and facilitation, as discussed at the NNEdPro Sixth International Summit on Nutrition and Health. Examples of applying clinical research into practice focused on the use of group consultations (also known as group clinics or shared medical appointments) to support behaviour change, the role of dietary micronutrients during the COVID-19 pandemic and the potential of Precision Nutrition. An emerging area from early implementation evidence includes group consultations, also known as shared medical appointments, as discussed by Dr Fallows. Group consultations have been shown to improve clinical outcomes for some patient groups (e.g., HbA1c, lipids, BMI), as well as improve self-care and health education, and patient and clinician satisfaction. These groups have been piloted throughout the UK both face-to-face and virtually, with initial findings suggesting they are feasible and acceptable to patients and clinicians. Further work is needed to assess whether these could be cost-effective when scaled-up in National Health Service UK primary care. During the COVID-19 pandemic, there has been increasing emphasis on the central role of nutrition in health, including the role of dietary micronutrients, as discussed by Dr Van Dael and Shane McAuliffe. Nutrition plays an important role in immunity, yet the nutritional status of the most vulnerable population groups is likely to deteriorate further due to the health and socio-economic impacts of the novel coronavirus. Thus, implementation of this evidence into health care practice is key. Precision Nutrition, defined as an ‘approach that uses information on individual characteristics to develop targeted nutrition advice, products or services’, offers an exciting opportunity to further individualise dietary advice for behaviour change, as discussed by Dr Kohlmeier and Dr Hernandez. Precision nutrition is underpinned by the recognition that individuals differ in many important ways due to identifiable molecular traits and can be utilised to determine personalised weight loss interventions based on genetic variants. Use of implementation science is in line with one of the six cross-cutting pillars of the Nutrition Decade: Aligned health systems for universal coverage of nutrition actions. Dr Bell, an Advanced Accredited Practising Dietitian in Australia, provided an overview of key implementation science models and frameworks. Implementation frameworks such as the Action Research Framework, the Knowledge to Action Cycle, and the Spread and Sustain Framework, are underpinned by knowledge creation, effective education, and culture change. Dr Bell then highlighted how theoretical frameworks have provided guidance for the implementation of real world, complex nutrition interventions, including the Systematised Interdisciplinary Program for Implementation and Evaluation (SIMPLE) in Australia, and the More-2-Eat program in Canada.

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)0.245
metaresearch head score (Gemma)0.374
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.245
Threshold uncertainty score0.932

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2450.374
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0050.003
Science and technology studies0.0060.022
Scholarly communication0.0250.030
Open science0.0060.028
Research integrity0.0230.025
Insufficient payload (model declined to judge)0.0350.013

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.

Opus teacher head0.176
GPT teacher head0.507
Teacher spread0.331 · 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.

Study designNot applicable
Domainnot available
GenreCommentary

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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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