Medical nutrition therapy in Canadian federal correctional facilities
Bibliographic record
Abstract
BACKGROUND: Under- and over nutrition as well as nutrition risk factors such as communicable and non-communicable diseases are a common and major cause of morbidity and mortality in correctional facilities. Consequently, medical nutrition therapy (MNT), a spectrum of nutrition services aimed at optimizing individual well-being, is being recognized as integral to the health of people who experience incarceration. However, there is a paucity of research that explores the delivery of MNT in correctional facilities. METHODS: A scoping review combined with secondary analysis of qualitative data (field notes, in-depth stakeholder interviews) from a 2-year ethnographic study about food insecurity and incarceration was undertaken to gain insights about the delivery of corrections-based MNT in Canada. Thematic analysis of all documents was done using an interpretive framework. RESULTS: An understanding about MNT was developed within three themes: 1) specialized service provision in a unique environment; 2) challenges with the provision of MNT; and 3) consideration of corrections-based MNT alternatives. An incarcerated individual's nutritional health was conceptualized as culminating from various factors that included dietary intake and health status, enabling environments, access to quality health services, and clinical nutrition services. Nutrition care practices, which range from health promotion to rehabilitation, are challenged by issues of access, visibility, adequacy, and environmental barriers. Their success is dependent on demand (e.g., ability of recipient to act) and factors that enable quality health and food services. Advancing corrections-based MNT will require policies that provide supportive food and health environments and creating sustainable services by integrating alternatives such as peer approaches and telehealth. CONCLUSIONS: Professional associations, government, researchers and other stakeholders can help to strengthen corrections-based MNT by fostering shifts in thinking about the role of health practitioners in these contexts, preparing future health professionals with the specialized skills needed to work in these environments, generating evidence that can best inform practice, and cultivating collaborations aimed at crime prevention, successful societal reintegration, and the reduction of recidivism.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.015 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".