Feasibility pilot of Cooking Together: Preliminary results
Bibliographic record
Abstract
BACKGROUND: Community-based programs, such as intergenerational programs, have been identified as a viable option to reduce dementia stigma and foster connections between generations. Cooking and eating with others is a social opportunity that bring people together. To date, few programs have been developed using food activities to connect community-dwelling persons living with dementia (PWD) and young adults (18-30 years old). Cooking Together is an innovative multi-week intergenerational cooking and nutrition program that was collaboratively developed and evaluated. The aim of this ongoing pilot study is to test a revised program model in three separate offerings (up to 10 participants/offering) to determine feasibility, usability of procedures and outcomes, and program utility. We report preliminary feasibility based on two offerings. METHOD: Weekly 2-hour sessions are chef-facilitated, using a brain-health focused menu. Feasibility was evaluated through recruitment (8-10 eligible participants per offering in 2 months), attendance (70% sessions attended after enrolment), and completion of pre- and post-program evaluation measures (85% participants). Young adults were recruited through a contact list of individuals who expressed interest in a previous offering but did not participate, campus advertisements, and word of mouth. PWD were recruited through a senior day program, repository of PWD interested in research studies, and word of mouth. RESULT: Ten young adults (age (median (range)): 19 (18-30), 90% women, 50% white) and 9 PWD (age (median (range)): 73 (36-80), 56% women, 89% white) were recruited over two offerings within a two-month time period. Long lead times to reserve kitchen space and scheduling meant that the offering start dates had limited flexibility, resulting in ongoing recruitment until the second week after the offering started. Fourteen participants (74%; 7 young adults and 7 PWD) completed both pre- and post-program measures. Average attendance at cooking sessions was 87%, with 40% attending the full program. Common reasons for non-attendance were prior commitments or illness. CONCLUSION: Preliminary findings from two offerings indicate that Cooking Together is feasible and acceptable. Recruitment for the third offering is underway, with data collection expected to be completed by April 2025. Findings from this study will inform future efficacy testing.
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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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".