695-P: Emotional Burden of Diabetes Varies by Demographic Factors, Management Strategies, and Duration of Diabetes
Bibliographic record
Abstract
The impact of diabetes on emotional well-being is well founded in the literature, though research on who experiences the brunt of this burden is lacking. The present study aims to quantify the emotional burden of diabetes and investigate the relationship between demographic factors, diabetes management, and severity of diabetes burden. From October-December 2022, 10,338 adults living with diabetes in the U.S., Canada, France, Germany, Italy, Netherlands, Sweden, and U.K. took an online survey in which they reported their diabetes technology and therapy usage and demographic information. Respondents also answered four 7-point questions about the effort and motivation behind their diabetes management and the degree to which they feel overwhelmed or burnt out by their diabetes. These items were averaged to create a composite variable on emotional burden (α = 0.85, M= 3.43, SD = 1.51). Subsequent responses were analyzed using SPSS; all significant findings are reported at p<0.05. Severity of emotional burden varies across several health-related and demographic factors. Respondents with T1D experience higher levels of emotional burden relative to those with T2D. Emotional burden is lowest among those who have had diabetes for 20 years or more and among those with an A1c ≤ 7%. People with diabetes in Canada report the highest severity, while those in Germany report the lowest. Emotional burden also varies by therapy and technology usage. CGM users experience greater emotional burden than non-users; however, there were no differences among pump users. Additionally, T2s on insulin experienced a greater degree of emotional burden than T2s not on insulin therapy. This research reveals several disparities in the mental burden of diabetes and suggests a link between intensity and duration of diabetes management and burden severity. Additional research is needed to identify avenues for support and burden reduction, targeted towards at-risk groups. Disclosure E.Cox: Employee; dQ&A. A.Zeng: Employee; dQ&A. E.Lin: Employee; dQ&A. E.Xu: Employee; dQ&A. T.Bell: Employee; dQ&A. T.L.Bristow: Employee; dQ&A.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".