Identifying attributes of food literacy: a scoping review
Bibliographic record
Abstract
OBJECTIVE: An absence of food literacy measurement tools makes it challenging for nutrition practitioners to assess the impact of food literacy on healthy diets and to evaluate the outcomes of food literacy interventions. The objective of the present scoping review was to identify the attributes of food literacy. DESIGN: A scoping review of peer-reviewed and grey literature was conducted and attributes of food literacy identified. Subjects included in the search were high-risk groups. Eligible articles were limited to research from Canada, USA, the UK, Australia and New Zealand. RESULTS: The search identified nineteen peer-reviewed and thirty grey literature sources. Fifteen identified food literacy attributes were organized into five categories. Food and Nutrition Knowledge informs decisions about intake and distinguishing between 'healthy' and 'unhealthy' foods. Food Skills focuses on techniques of food purchasing, preparation, handling and storage. Self-Efficacy and Confidence represent one's capacity to perform successfully in specific situations. Ecologic refers to beyond self and the interaction of macro- and microsystems with food decisions and behaviours. Food Decisions reflects the application of knowledge, information and skills to make food choices. These interdependent attributes are depicted in a proposed conceptual model. CONCLUSIONS: The lack of evaluated tools inhibits the ability to assess and monitor food literacy; tailor, target and evaluate programmes; identify gaps in programming; engage in advocacy; and allocate resources. The present scoping review provides the foundation for the development of a food literacy measurement tool to address these gaps.
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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 | 0.034 | 0.175 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.035 | 0.028 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".