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Record W3093739194 · doi:10.1017/s0029665120007442

Barriers and facilitators to nutritional risk screening in primary care and intervention components to address these barriers and facilitators

2020· article· en· W3093739194 on OpenAlexaffabout
Christine Marie Mills

Bibliographic record

VenueProceedings of The Nutrition Society · 2020
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsQueen's University
Fundersnot available
KeywordsIntervention (counseling)Primary careAction (physics)Content (measure theory)MedicineMedical educationNursingFamily medicine

Abstract

fetched live from OpenAlex

One-third of community-dwelling Canadians aged 65 and older are at increased nutritional risk, the risk of poor dietary intake and nutritional status 1 with consequences including increased frailty, decreased quality of life, increased hospitalization, and higher mortality rates 1 .Identification and treatment can mitigate these outcomes.Nutritional risk starts in the community, making primary care the ideal location for nutritional risk screening 2 .Understanding barriers and facilitators to nutritional risk screening in primary care and identifying intervention components to address them is therefore important.The peer-reviewed and grey literature were searched for these barriers and facilitators using "aged OR senior* OR older adults" AND "nutrition* risk OR malnutrition OR undernutrition" AND "screen*" AND "community OR general practice OR primary care."The databases PubMed, Ovid MEDLINE, and Cumulative Index to Nursing & Allied Health were searched.The Cochrane Library, National Institute for Clinical Evidence, Guidelines International Network, Guideline Central, Practice-Based Evidence in Nutrition, and Dietitians of Canada websites were also searched.A regular Google search was then performed, with the first ten pages of search results reviewed.Publications were screened for relevance.Key informants consisting of health care professionals working in primary care were asked to identify additional barriers and facilitators and intervention components.The Theoretical Domains Framework (TDF) 3 was used to classify the barriers and facilitators.Intervention components were identified from the Effective Practice and Organisation of Care (EPOC) taxonomy 4 .Nine relevant barriers and nine relevant facilitators were identified.They were located within the following 12 domains of the TDF: knowledge; skills; social/professional role and identity; beliefs about capabilities; beliefs about consequences; motivation and goals; environmental context and resources; social influences; emotions; memory, attention and decision processes; behavioural regulation; and nature of the behaviours.Regarding intervention components from the EPOC taxonomy, educational materials and meetings can address the first nine of the 12 listed previously.Inter-professional education can address social/professional role and identity; and motivation and goals.Reminders can address memory, attention, and decision processes; and environmental context and resources.Patient-mediated interventions can address environmental context and resources; and nature of the behaviours.Local opinion leaders can address social influences; and environmental context and resources.Communities of practice can address social influences.Tailored interventions and local consensus process can address behavioural regulation.The TDF can examine the barriers and facilitators to nutritional risk screening of older adults in primary care.The EPOC taxonomy can identify intervention components to address them.Identification and classification of these barriers and facilitators and identification of intervention components can aid in the development and implementation of interventions designed to improve rates of nutritional risk screening in primary care.Identification of nutritional risk before it progresses to malnutrition may reduce morbidity and mortality.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.162
Threshold uncertainty score0.681

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.025
GPT teacher head0.287
Teacher spread0.262 · 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 teacher head, 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".

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Citations0
Published2020
Admission routes2
Has abstractyes

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