Development and Pilot Testing of a Healthy Eating Video-Supported Program for Adults with Developmental Disabilities
Bibliographic record
Abstract
Video technology is a potentially effective means to teach individuals with developmental disabilities (DD) about healthy eating. Research in this area, however, is relatively unexplored. This study developed and tested a video intervention to teach healthy eating to adults with DD. A 5-segment educational video, an accompanying workbook, and a facilitator guide were developed to teach basic healthy eating concepts to adults with DD. Twelve adults with DD took part in a 5-week educational program led by trained facilitators using the materials created. Pre- and posttests were used to measure knowledge gained from participating in the intervention. Seventy-five percent (n = 9) of participants improved their knowledge scores, 8% (n = 1) maintained residue knowledge, and 17% (n = 2) had a decrease in their score. Video instructions can be an effective intervention modality to increase knowledge in adults with DD about healthy eating. Key enablers identified for participants' knowledge gain included video content developed based on the learning need and cognitive level of intended users; program delivered by facilitators trained in effective teaching strategies; and engaging the participants' staff, family, and caregivers to provide ongoing reinforcement about healthy eating.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".