Diabetes Self-care in Daily Life: A Qualitative Study on Goal Conflict Management in Emerging Adults With Type 1 Diabetes
Bibliographic record
Abstract
OBJECTIVES: Our aim in this study was to gain further insight into the way emerging adults with type 1 diabetes experience and manage conflicts between their desire to take good care of their diabetes and reaching daily goals in other life domains. METHODS: Participants were recruited from 3 locations of a Dutch type 1 diabetes care and research centre until researchers considered data saturation to have been reached. Sixteen emerging adults were interviewed and completed a diary for 3 days. Interview transcripts were analyzed by 2 researchers, using directed content analysis. RESULTS: Participants had daily conflicts between self-management and attaining goals in various life domains (including food, alcohol, sports, school/work, and leisure activities); social goals figured prominently. Generally, participants first tried to combine their diabetes goal and other goals by means of flexible self-care supported by modern technology, thorough planning, and social support. When combining was not possible, most participants tended to prioritize nondiabetes goals, unless they perceived a high risk for adverse health outcomes. Prioritization of diabetes goals, as well as nondiabetes goals, often resulted in negative emotions such as sadness and guilt. CONCLUSIONS: For emerging adults, good care of diabetes is a challenge to undertake while also addressing other goals of daily life. Combination and prioritization strategies both play a role in efforts to deal with these goal conflicts, although the latter often trigger negative emotions. The present findings can be used for the optimization of diabetes self-management support by taking the broader life context into account.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".