MULTIMORBIDITY AND LONELINESS AMONG CANADIAN OLDER ADULTS: THE MEDIATING EFFECT OF PAIN PERCEPTION
Bibliographic record
Abstract
Multimorbidity negatively affects the activities, lifestyle and quality of life of older persons causing complex interactions between physical and psychological conditions. These may make social interaction difficult, leading to potential feelings of loneliness. However, it is not known how the pathways between multimorbidity and loneliness could be modulated by the perception of pain. This study aimed to determine if an association exists between multimorbidity and loneliness and whether this association is mediated by pain perception. This cross-sectional study used data drawn from the 2008/2009 Canadian Community Health Survey, targeting persons aged ≥80 (n=6,427). Loneliness scale was derived by summing up responses to questions measuring loneliness (Hughes et al., 2004), multimorbidity was measured using an additive multimorbidity scale and pain was assessed with the HUPDPAD variable in CCHS. Ordinary least square regression analysis with six hierarchical blocks was used to estimate the relationships among multimorbidity, loneliness and pain variables. Multimorbidity expresses a statistically significant beta coefficient with the loneliness scale (β=0.092, p<0.001) in block 3, after controlling for age, sex, marital status, education and income. The inclusion of perceived pain in block 4 reduced the effect of multimorbidity on loneliness to β=0.049 (p<0.001). Inclusion of functional status in block 5 further reduced this association to β=0.043 (p=0.001). In this study, multimorbidity modestly increases the risk of loneliness among older persons as hypothesized, while perceived pain appears to slightly mediate this effect. Further study is needed to help clarify these associations using more refined measures and other sub-populations.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".