Retrospective cross-sectional study examining the association between loneliness and unmet healthcare needs among middle-aged and older adults using the Canadian Longitudinal Study of Aging (CLSA)
Bibliographic record
Abstract
OBJECTIVES: Our primary objective was to estimate the association between loneliness and unmet healthcare needs and if the association changes when adjusted for demographic and health factors. Our secondary objective was to examine the associations by gender (men, women, gender diverse). DESIGN, SETTING, PARTICIPANTS: Retrospective cross-sectional data from 44 423 community-dwelling Canadian Longitudinal Study on Aging participants aged 45 years and older were used. PRIMARY OUTCOME MEASURE: Unmet healthcare needs are measured by asking respondents to indicate (yes, no) if there was a time when they needed healthcare in the last 12 months but did not receive it. RESULTS: In our sample of 44 423 respondents, 8.5% (n=3755) reported having an unmet healthcare need in the previous 12 months. Lonely respondents had a higher percentage of unmet healthcare needs (14.4%, n=1474) compared with those who were not lonely (6.7%, n=2281). Gender diverse had the highest percentage reporting being lonely and having an unmet healthcare need (27.3%, n=3), followed by women (15.4%, n=887) and men (13.1%, n=583). In our logistic regression, lonely respondents had higher odds of having an unmet healthcare need in the previous 12 months than did not lonely (adjusted odd ratios (aOR) 1.80, 95% CI 1.64 to 1.97), adjusted for other covariates. In the gender-stratified analysis, loneliness was associated with a slightly greater likelihood of unmet healthcare needs in men (aOR 1.90, 95% CI 1.64 to 2.19) than in women (aOR 1.73, 95% CI 1.53 to 1.95). In the gender diverse, loneliness was also associated with increased likelihood of having an unmet healthcare need (aOR 1.38, 95% CI 0.23 to 8.29). CONCLUSIONS: Loneliness was related to unmet healthcare needs in the previous 12 months, which may suggest that those without robust social connections experience challenges accessing health services. Gender-related differences in loneliness and unmet needs must be further examined in larger samples.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".