Health literacy, literacy, numeracy and nutrition label understanding and use: a scoping review of the literature
Bibliographic record
Abstract
BACKGROUND: Low health literacy, literacy and numeracy have been identified as barriers to consumer understanding and the interpretation of nutrition-related information. To inform policy and dietetic practice, we examined the extent, range and nature of research on empirical relationships between health literacy, literacy or numeracy and the understanding and use of nutrition labels. METHODS: A scoping review of the literature was conducted. A search of eight databases on 15 April 2014 and 26 May 2016 returned 651 and 173 records, respectively. After de-duplication and two levels of relevance screening, 16 studies were deemed eligible for inclusion in the present review. RESULTS: The majority of studies were conducted in the USA and focused primarily on the use of back-of-pack nutrition labels. Empirical relationships reported between health literacy and nutrition label use were inconsistent and, in some cases, contradictory. The findings from studies examining empirical relationships between literacy, numeracy and nutrition label use suggest that consumers with lower literacy and numeracy: (i) differ from those with higher levels in some of the judgements that they make about food and (ii) may benefit from interventions designed to improve their understanding and use of nutrition label information. Measurement-related issues were identified, such as a reliance on self-reports of nutrition label use, as well as a lack of independence between some measures of health literacy and nutrition label understanding and use. CONCLUSIONS: The empirical relationships between health literacy, literacy, numeracy and nutrition label understanding and use have not been well-studied. Additional attention is needed regarding the measurement-related issues identified in the present review.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".