Diet Adherence among Adults with Type 2 Diabetes Mellitus: A Concept Analysis
Bibliographic record
Abstract
Objectives: To analyze the concept of diet adherence and its components in the management of type 2 diabetes mellitus (T2DM). Methods: The Walker and Avant (2011) method of concept analysis was used. Scientific databases were queried for research articles in the English language published during 2010-2020 using the search terms: compliance, adherence, treatment adherence, diet adherence, T2DM, and concept analysis. The tools that measure diet adherence and its attributes were identified and evaluated. Results: The concept of diet adherence implies the process of following a diet plan by means of self-monitoring, maintaining, and preventing relapses. Diet adherence is facilitated by antecedents which comprise motivation, understanding the dietary recommendations, developing appropriate health beliefs, self-efficacy, setting achievable goals, and receiving social support. Successful diet adherence brings consequences in health as reflected in improved T2DM-specific clinical parameters and enhanced health-related quality of life. Conclusions: Patients with T2DM often have poor diet adherence due to failure to understand, implement, and maintain the required antecedents, such as motivation, understanding, health beliefs, self-efficacy, practical goals, and social support. Healthcare providers need to ensure that the patients understand the concept of diet adherence and implement it in their daily lives. Further research is needed into diet adherence and its components to evolve more effective measures to be communicated to T2DM patients.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".