The Relationship Between Multimorbidity and Self-Reported Health Among Community-Dwelling Older Adults and the Factors that Shape This Relationship: A Mixed Methods Study Protocol Using CLSA Baseline Data
Bibliographic record
Abstract
Self-reported health is a common measure predictive of morbidity and mortality among adults. Many factors are known to be associated with self-reported health including the number of chronic conditions (i.e., multimorbidity). While the association between self-reported health and morbidity and mortality has been well-established, the factors that shape the relationship with self-reported health (e.g., modify and mediate) are poorly understood. Further, it is unknown why some older adults, despite having high numbers of chronic conditions, continue to rate their health positively. This is known as the well-being paradox. This mixed methods research study was designed to address these knowledge gaps. The objectives of the proposed research are to (1) determine what factors shape the relationship between multimorbidity and self-reported health and how they do so; (2) describe the ways that older adults define and perceive their individual health; and (3) explain the well-being paradox. Informed by a multimorbidity resilience framework, the quantitative component of research will analyze Canadian Longitudinal Study on Aging data while the qualitative component will collect and analyze interview data from 12 to 20 community-dwelling older adults using a case study design. Findings from this study have the potential to inform and advance future health intervention programs or services aimed at improving health-related quality of life for community-dwelling older adults.
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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 | 0.065 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".