Social Determinants and Self-Care for Making Good Treatment Decisions and Treatment Participation in Older Adults: A Cross-Sectional Survey Study
Bibliographic record
Abstract
Background: Community-dwelling adults who can perform self-care behaviors related to making treatment decisions and participating in treatment have been found to use less emergency care. In this exploratory study, we examined the relationships in older adults between five social determinants (urban/rural residence, sex, age, marital status, and education) and the perceived importance, desirability, and ability to perform 11 self-care behaviors related to making good treatment decisions and participating in treatment. Methods: This cross-sectional study surveyed 123 community-dwelling older adults living in the southern United States in 2015–2016. All participants were 65 years or older. Data were collected using the Patient Action Inventory for Self-Care and analyzed using descriptive, univariate, and multivariate logistic regression analyses. Results: The social determinants (identified as barriers) of self-care behaviors related to making good treatment decisions and participating in treatment were: having less than a high school education, being 75 years or older, and being separated from a spouse. Sex and residence were found to be neither barriers nor facilitators. Conclusions: Our findings suggest that, in older adults, attending to the needs related to health literacy education and improving social support might increase self-care behaviors related to making good treatment decisions and participating in treatment. Future research will compare the differences across diverse populations to validate our study findings.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".