Self-management difficulties in Swedish older adults and associations with sociodemographic factors, number of conditions, depression and health status
Bibliographic record
Abstract
Objective This study describes patterns of self-management ease and difficulty among older adults with long-term health conditions and the associations with gender, level of education, number of conditions, depression and/or health status.Materials and methods Cross-sectional data were collected between 2021–2022 in a municipality in northern Sweden. The survey included demographic and health-related questions. To assess self-management ease or difficulty and symptoms of depression, the Patient Reported Inventory of Self-Management of Chronic Conditions (PRISM-CC) and the Geriatric Depression Scale were used. 516 older adults between 72–73 years of age with long-term health conditions were included. Descriptive statistics and logistic regression were used to describe patterns of self-management ease and difficulty and to examine which factors were associated with self-management difficulty.Results Most older adults did not experience self-management difficulty. There were, however, differences between the seven PRISM-CC domains. The Internal domain (managing negative emotions and stress) had the highest percentage (25.39%) of older adults with self-management difficulty. In all domains, there was also a subgroup of individuals (n = 26) that had noticeably lower PRISM-CC scores (more difficulty). A strong association between having depressive symptoms or having poor health status and self-management difficulty was found.Conclusion This study highlights the need for regular mental health screenings and individualized self-management support for older adults. Future research should explore intervention strategies that integrate mental health support into self-management programs for individuals with long-term health conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".