The Older Adult Nutritional Ecosystem in Western Newfoundland and Examples of Community-based Nutrition Programming
Bibliographic record
Abstract
Context: Newfoundland and Labrador (NL) has an aging population (Statistics Canada, 2019) and high levels of chronic disease amongst older adults (Government of NL, 2017). Nutrition directly influences health (World Health Organization, 2015) and the nutrition of older adults living in NL is of low quality (Sarkar et al., 2015). Nutrition in NL is affected by the high cost, limited in-province production, and reliance on import via marine and truck transport for approximately 90% of consumed food (Food First NL, 2015). Objective: The aim of the research project was to explore the individual and environmental factors that influence older adult nutrition and to understand areas for potential change that can be supported by community-based organizations and nursing practice. Study Design and Analysis: Qualitative research study guided by Interpretative Description methodology (Thorne, 2016) and the theoretical perspective of the Social Ecological Model (Stokols, 1996; 2000). A community-engaged approach with community partner organizations that are directly involved in older adult nutrition. Setting: An area along the west coast of Newfoundland, the most eastern province in Canada. Population Studied: Engagement with older adults, key informants, and nurses living in western NL. Intervention/Instrument: Through review of the literature, focus groups with older adults, and individual semi-structured interviews with key informants and nurses. Outcome Measures: In-depth understanding about the individual and environmental factors that impact the nutrition of older adults. Working with community organizations to develop programming to achieve positive nutritional change. Results: A visual map of the older adult nutritional ecosystem in western NL that identifies specific individual and environmental factors that influence older adult nutrition. Individual factors that impact older adult nutrition include availability and affordability of food, time and planning, motivation, and health. Environmental factors include climate, distribution systems, finances, government policy, and social groups. A tool kit of community-based nutrition programming, including examples that were recommended by study participants and examples of programing already being implemented by community groups to positively impact older adult nutrition, highlighting the creation of an academic-community research collective and a new health and wellness facility with a community kitchen.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".