Increases in fruit intakes in older low consumers of fruit following two community-based repeated exposure interventions
Bibliographic record
Abstract
The present study investigated the value of two repeated exposure interventions for increasing intakes of fruit in older people. A total of ninety-five participants (aged 65 years and over) were randomised to receive either one (E1), five (E5) or five plus (E5+) exposures to fruit over a 5-week period. Fruit exposures occurred in community-based church and social groups, through fruit-tasting sessions involving familiar fruits and novel fruit products and dishes (E1, E5, E5+), and through fruit provision (E5+). Daily intakes of fruit and vegetables were assessed before and after all interventions. Liking for all fruits was also measured during repeated exposure (E5, E5+). In low consumers of fruit (one portion/d or less), fruit intakes increased significantly in the repeated exposure groups (E5, E5+) (t(30) = 5·79, P< 0·01), but did not change in the E1 group (t(16) = 0·29, P= 0·78). No differences were found between E5 and E5+ groups (F(3,87) = 1·22, P= 0·31). Similar effects were also found in fruit and vegetable intakes. No effects were found in other participants. Also, no changes in liking were found. These findings suggest that compared to single exposure, repeated exposure to fruit via fruit-tasting sessions once per week for 5 weeks in a community setting significantly improved fruit intakes, and fruit and vegetable intakes in older low consumers of fruit, although no benefits of additional fruit provision were found. Repeated exposure was also easy to implement, of low cost and enjoyable.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".