Transitional Needs of Australian Young Adults With Type 1 Diabetes: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Young adulthood is marked by transitions that impact diabetes self-management behaviors, which require ongoing diabetes education and support. Traditional diabetes education programs and services currently do not meet the needs of many young adults with type 1 diabetes mellitus (T1DM) as they continue to fall through the cracks of clinical services. Age-centered diabetes education programs and services present an opportunity for young adults to meet in a supportive environment and gain a better understanding about diabetes management. OBJECTIVE: The aim of the study was to identify the health and well-being needs of Australian young adults aged between 18 and 35 years with T1DM to develop appropriate solutions to keep them engaged with diabetes self-management. METHODS: In total, 13 semistructured individual interviews and self-reported surveys were obtained to understand participants' experiences with diabetes education programs and services. Together with survey data, transcribed interviews were analyzed into themes and categories using comparative analysis to identify the health and well-being needs of young adults with T1DM during young adulthood. RESULTS: Diabetes education and service needs for young adults with T1DM related to improving access to existing diabetes education programs and services, having credible informational resources, as well as having personalized diabetes management advice. Participants especially valued relevant and real-time information and opportunities for peer support, mostly sourced from Web-based platforms. CONCLUSIONS: There is a need for diabetes education programs and services to be age-appropriate and easily accessible, to provide relevant and credible information, and to provide opportunities for peer support to better support young adults with T1DM. These findings also support the use of diabetes education programs or services delivered online through mHealth systems in this population.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".