P1‐414: Promoting Adoption of Brain Healthy Eating Patterns: A Pilot Study Using Problem‐Solving Training
Bibliographic record
Abstract
In the absence of disease-modifying drugs, there is need to embed non-pharmacologic risk reduction strategies into primary care and public health policy to help lower Alzheimer’s disease prevalence. While the Alzheimer’s Association, USA, recently argued that ‘there is sufficiently strong evidence to conclude that a healthy diet and lifelong learning/cognitive training may also reduce the risk of cognitive decline’, supporting middle aged and older adults to undertake sustained lifestyle behaviour change is challenging. As part of the Canadian Consortium on Neurodegeneration in Aging (CCNA), we will be conducting a combined diet and exercise clinical trial. In preparation for this larger randomized trial, the current study piloted a novel approach to promoting adoption of brain healthy eating patterns: combining education with problem-solving training that involves individualized goal-setting and planning in a group context. Design: In-depth qualitative descriptive design. Participants: Five older women (65+) with no known cognitive deficits; recruited from Baycrest volunteer pool. Measures: Semi-structured interviews administered post-intervention. Intervention: A combined education, individualized problem-solving approach conducted over five, 2-hour group sessions. Education focused on providing information about brain healthy eating Analysis: Interviews were transcribed and analyzed using thematic analysis, which involves an iterative approach of becoming familiar with the data, assigning and then reviewing codes to generate themes and then connecting themes to provide an account for the data. Coding and theme development was discussed with the full research team at several stages. Preliminary analyses revealed some lack of clarity regarding the purpose of the group. All participants reported a high level of satisfaction with the didactic part of the program and four/five found the individualized goal setting useful and helpful. Most felt the groups required more structure. Four/five reported feeling positive about behaviour changes made and optimistic about maintaining the changes. Providing educational material to help focus dietary change is a desirable and needed aspect of a nutrition intervention. Nevertheless, a well focused and clear approach to self-directed goal setting appears to be a necessary component of the intervention to support an individual in making longer term sustainable changes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".