Impact of Unmet Care Needs on Resilience in Multimorbid Older Adults
Bibliographic record
Abstract
Abstract Older adults with multimorbidity (i.e., two or more chronic conditions) are more likely to face an enduring risk of unmet (care) needs in accessing healthcare services due to their unique and complex form of health adversity. Multimorbidity resilience (MR), the ability to bounce back from multiple chronic illness-related challenges, is an important indicator of positive adaptation to multimorbidity. However, despite the importance of addressing unmet needs among multimorbid older adults, there is sparse knowledge on the impact of unmet needs on resilience. This study aims to fill these gaps through an investigation of the key factors affecting resilience among multimorbid older adults. A social determinant of health model is used to frame the study. Using the Canadian Longitudinal Study on Aging (CLSA), a total of 12,677 multimorbid older adults were analyzed. Hierarchical linear regression was conducted to identify the relationship between resilience and four sequentially ordered blocks of predictors: 1) unmet needs, 2) sociodemographic characteristics, 3) health context, and 4) social/environmental supports. It was found that the level of resilience was lower among multimorbid older adults who reported having unmet needs compared to those without unmet needs. Several significant covariates across multiple domains were also identified (e.g., sex, urban/rural status, household income, perceived healthy aging, community friendliness). This study extends our understanding of potential protective and/or risk factors of MR among older adults. To address the challenges of accessing healthcare services among multimorbid older adults, there is a need to implement health policies/initiatives to fill their unmet care needs.
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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 | 0.001 | 0.007 |
| 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 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".