The Experience of a Guided Self-determination Intervention on Diabetes Distress Among Adults With Type 2 Diabetes
Bibliographic record
Abstract
OBJECTIVES: Type 2 diabetes (T2D) can cause psychosocial difficulties, burdensome self-care, and stigmatization, which can lead to diabetes distress, reduced quality of life, and suboptimal diabetes management. Guided self-determination (GSD) has been identified as valuable in alleviating diabetes distress. In this study we explored the experience of a GSD intervention among people with T2D and aimed to understand the potential impact on diabetes distress. METHODS: A qualitative study was undertaken using semistructured interviews (n=10) and reflexive thematic analysis by Braun and Clarke. The study adhered to the Consolidated Criteria for Reporting Qualitative Studies checklist. RESULTS: Four themes were revealed: "Person-centred reflection creates self-insight," becoming aware of how one deals with diabetes and finding renewed optimism; "Unburdening myself," characterized by room and support for psychosocial aspects; "Making sense of diabetes: Creating meaning through reflection and dialogue," reflecting on current difficulties while simultaneously making sense of diabetes; and "Barriers to changes: Between acceptance, effort, and everyday realities," where readiness, motivation, and energy impacted possible changes. CONCLUSIONS: GSD may improve the ability to alleviate stressors related to diabetes. However, some individuals experienced no changes. Readiness, motivation, and energy appear to be critical for the potential to change.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| 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".