Contextually Appropriate Tools and Solutions to Facilitate Healthy Eating Identified by People with Type 2 Diabetes
Bibliographic record
Abstract
Type 2 diabetes (T2D) is a complex, multifaceted disease and its treatment involves lifestyle intervention (LI) programs that participants may find difficult to adopt and maintain. The objective of this study is to understand the lived experiences of participants with T2D regarding healthy eating behavior change, in order to identify and incorporate relevant information, skills, and educational approaches into LI programs. An explorative qualitative study was undertaken. Purposeful sampling was used to recruit 15 participants. One-on-one, semi-structured, open-ended, and in-depth interviews were conducted. An essentialist paradigm was adopted to accurately report the experiences, meaning, and reality of participants. An inductive approach was used to analyze the data. Participants reported that being diagnosed and living with T2D could be overwhelming, and their ability to manage was influenced by health care providers (HCP), family, and individual context. Many experienced a loop of "good-bad" eating behaviors. Participants expressed desires for future diabetes management that would include program content (nutrition, physical activity, mental health, foot care, and consequences of T2D), program features (understand context, explicit information, individualized, hands-on learning, applicable, realistic, incremental, and practical), program components (access to multidisciplinary team, set goals, track progress and be held accountable, one-on-one sessions, group support, maintenance/follow-up), and policy change. In conclusion, the results of this study indicate that T2D management requires more extensive, comprehensive, and ongoing support, guided by the individual participant.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".