Impact of a Pilot Intervention to Improve Nutrition Knowledge and Cooking Confidence Among Low-Income Individuals
Bibliographic record
Abstract
Poor dietary behaviors, limited food preparation skills, and low levels of self-efficacy toward preparing healthy meals have been indicated in low-income and food insecure populations. The purpose of this pilot intervention was to determine the effectiveness of a community cooking demonstration at increasing participants’ general nutrition knowledge and confidence to prepare healthy meals with limited resources. Data was analyzed from a convenience sample of 23 low-income adults associated with Head Start (n=8) or a local soup kitchen (n=15) in the Midwestern United States. Participants attended a one-hour presentation comprised of a cooking demonstration, taste testing, and basic education on the MyPlate food guide and food safety. Subjects completed a pre- and post-assessment to measure changes in cooking confidence and general food and nutrition knowledge. Results indicated that, although there were no significant improvements in participants’ confidence to prepare healthy meals (39.3±11.3 vs. 44.5±9.1; t=1.76, p=0.25), subjects experienced significant gains in knowledge related to the MyPlate food guide (1.2±0.5 vs. 1.8±0.8; t=2.82, p=0.01) and basic food safety (0.7±0.9 vs. 2.5±1.0; t=6.05, p<0.01). Further research is necessary to identify effective strategies for parlaying increased nutrition knowledge into improved self-efficacy and behavior change.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".