The Ontario Food and Nutrition Strategy: identifyingindicators of food access and food literacy for earlymonitoring of the food environment
Bibliographic record
Abstract
INTRODUCTION: To address challenges Canadians face within their food environments, a comprehensive, multistakeholder, intergovernmental approach to policy development is essential. Food environment indicators are needed to assess population status and change. The Ontario Food and Nutrition Strategy (OFNS) integrates the food, agriculture and nutrition sectors, and aims to improve the health of Ontarians through actions that promote healthy food systems and environments. This report describes the process of identifying indicators for 11 OFNS action areas in two strategic directions (SDs): Healthy Food Access, and Food Literacy and Skills. METHODS: The OFNS Indicators Advisory Group used a five-step process to select indicators: (1) potential indicators from national and provincial data sources were identified; (2) indicators were organized by SD, action area and data type; (3) selection criteria were identified, pilot tested and finalized; (4) final criteria were applied to refine the indicator list; and (5) indicators were prioritized after reapplication of selection criteria. RESULTS: Sixty-nine potential indicators were initially identified; however, many were individual-level rather than system-level measures. After final application of the selection criteria, one individual-level indicator and six system-level indicators were prioritized in five action areas; for six of the action areas, no indicators were available. CONCLUSION: Data limitations suggest that available data may not measure important aspects of the food environment, highlighting the need for action and resources to improve system-level indicators and support monitoring of the food environment and health in Ontario and across Canada.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".