An evaluation of <i>SecondBite<sup>®</sup>'s FoodMate<sup>®</sup></i>, a nutrition education and skill‐building program aimed at reducing food insecurity
Bibliographic record
Abstract
Abstract Issue addressed Enhancing food skills and nutrition knowledge may help promote healthy eating among people who are food insecure. FoodMate® by SecondBite®, an 8‐week nutrition education and food hamper program, focuses on developing food skills and independence among Australians at risk of/experiencing food insecurity. This study aimed to explore participants' perceptions of and experiences with FoodMate® over a long‐term (up to 2 years) follow‐up. Methods For evaluation purposes, SecondBite® previously collected data from participants prior to (T1) and following completion (T2) of FoodMate®. This paper reports results from semi‐structured telephone interviews conducted in a follow‐up study (2016/2017, T3) among 19 adults enrolled in FoodMate® programs delivered in Victoria and New South Wales within the previous two years. Data were analysed using inductive thematic analysis of responses to T3 open‐ended questions, and descriptive analysis of closed‐ended question responses (T1 vs T2 vs T3). Results Major qualitative themes included program enjoyment; perceived positive long‐term program impact on participants' eating and related attitudes and skills; barriers to cooking; suggested program modifications; and impact on others. In descriptive quantitative analyses, participants' diet; confidence to cook using basic ingredients, follow simple recipes and try new foods; cooking and food‐related skills; social engagement and life satisfaction all improved between T1 and T3. Conclusions Overall, FoodMate® was well‐received and associated with long‐term positive changes in a range of outcomes. Wider implementation among vulnerable groups should be considered. So what? Future health promotion initiatives could adopt FoodMate® to increase food skills and knowledge among adults experiencing food insecurity.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".