Determinants of Readiness for Adopting Healthy Lifestyle Behaviors Among Indigenous Adolescents with Type 2 Diabetes in Manitoba, Canada: A Cross‐Sectional Study
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to determine whether the readiness for adopting healthy lifestyle behaviors was associated with mental health and comorbid conditions in youth with T2D. METHODS: A cross-sectional comparison of various measures of mental health (distress, stress, resilience) and comorbid conditions (glycated hemoglobin, adiposity, hypertension) was conducted within a cohort of indigenous youth with T2D living in Canada, stratified according to their readiness to adopt healthy lifestyle behaviors based on Prochaska's transtheoretical model. RESULTS: Within the entire cohort (n = 162), only 14% were considered ready to adopt all healthy lifestyle behaviors. The readiness to adopt all lifestyle behaviors was associated with higher positive mental health (47 vs. 39 units; P < 0.05) and sense of mastery (40 vs. 37 units; P < 0.05), lower perceived stress (27 vs. 29 units; P < 0.05) and distress (8 vs. 10 units; P < 0.05), and better glycemic control (HbA1c: 8.4 ± 2.6% vs. 9.7 ± 2.8%; P < 0.05) compared with youth who were not ready to adopt all lifestyle behaviors. CONCLUSIONS: The readiness for adopting healthy lifestyle behaviors is low among adolescents with T2D. Being ready to adopt healthy lifestyle behaviors is associated with better mental health and glycemic control.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".