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P3-02-03 - The predictors of interpregnancy change in body mass index.

2019· preprint· en· W4285786111 on OpenAlexaboutno aff
Ciara Reynolds

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicCancer Risks and Factors
Canadian institutionsnot available
Fundersnot available
KeywordsIndex (typography)Body mass indexMedicineInternal medicineComputer science

Abstract

fetched live from OpenAlex

Introduction: Over one-third of community-dwelling older adults in Canada are at increased nutritional risk. Worldwide, two-thirds of older adults are at increased nutritional risk, although this figure includes those who are hospitalized or in long term care. Nutritional risk can lead to malnutrition; this occurs when an individualu2019s food intake has an imbalance of energy, protein, or other nutrients. Nutritional risk and malnutrition are associated with poor quality of life, increased hospitalization, and premature mortality. Since malnutrition starts in the community, primary care is the ideal location for nutritional risk screening. If nutritional risk is identified early, before it progresses to malnutrition, it can be more easily treated. It is therefore important to understand barriers and facilitators to nutritional risk screening in primary care.Materials and Methods: The peer-reviewed and grey literature were searched. The databases CINAHL, Embase, Medline, and Google Scholar were used to identify articles related to barriers and facilitators to nutritional risk screening of older adults in primary care. A Google search identified publications from the grey literature related to nutritional risk screening of older adults. Key informants consisting of health care professionals working in primary care were asked to identify additional barriers. The Theoretical Domains Framework (TDF) was used to classify the barriers and facilitators. Results: Nine barriers and nine facilitators relating to nutritional risk screening of older adults in primary care were identified. These barriers and facilitators were located within the following domains of the TDF: knowledge; skills; social/professional role and identity; beliefs about capabilities; beliefs about consequences; motivation and goals; memory, attention and decision processes; environmental context and resources; social influences; emotions; behavioural regulation; and nature of the behaviours. Discussion: The TDF can be used to examine the barriers and facilitators to nutritional risk screening of older adults in primary care. Identification and classification of these barriers and facilitators can aid in the development and implementation of interventions designed to improve rates of nutritional risk screening in primary care. Identifying older adults at nutritional risk can help to prevent malnutrition, by intervening early when poor dietary intake may still be relatively easy and inexpensive to address. Screening is the first step in this identification.

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)0.002
metaresearch head score (Gemma)0.026
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.002
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0300.003

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 head0.031
GPT teacher head0.312
Teacher spread0.281 · 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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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