Ingredients for Success: Strategies to Support Local Food Use in Health Care Institutions
Bibliographic record
Abstract
There is growing interest in use of local food within health care institutions such as hospitals and long-term care homes. This study explored stakeholder perspectives on (i) influences on local food use and (ii) strategies that support success and sustainability of use in health care institutions. Fifteen participants who were institutional leaders with experience in implementing or supporting local food use in health care institutions in Ontario were recruited through purposeful and snowball sampling. A semi-structured interview was conducted by telephone and audio-recorded. Qualitative content analysis identified that influences on local food use were: product availability, staff and management engagement, and legislation and resources (e.g., funding, labour). Several strategies were offered for building and sustaining success including: setting goals, requesting local food availability from suppliers, and more clearly identifying local foods in product lists. The influences and potential strategies highlighted in this paper provide a greater understanding for dietitians and food service managers on how local foods can be incorporated into health care institutions.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".