Bridging the Gap: Access to Care, Diagnostic Concordance, and Self-Rated Health in Older Adults
Bibliographic record
Abstract
Abstract Self-rated health (SRH) is a widely utilized indicator of population health, shaped by multiple domains. Prior research has established that functioning, diseases, and pain are the most influential contributors to SRH in Canada, particularly among older adults. However, the role of access to care in shaping the diagnosis domain—and by extension, its influence on SRH—remains underexplored. We use the TorSaDE cohort, a linked dataset combining five cycles of the Canadian Community Health Survey (CCHS 2007-2016) with administrative health data covering the entire population of the province of Québec, Canada, enabling comprehensive analysis of health trajectories and outcomes among community-dwelling older adults. Using these linked data, we categorize participants into three diagnostic groups: (1) self-reported diagnosis only, (2) administrative (algorithmic) diagnosis only, and (3) concordant diagnosis across both sources. We conducted these analyses for both diabetes and major neurocognitive disorders (MNCD), and we examined the differences in self-ratings of health between these groups, and across a range of sociodemographic characteristics. We found that individuals with only algorithmically detected diagnoses reported significantly better self-rated health compared to the other two groups. This pattern was consistent for both diabetes and MNCD. These findings provide strong support for the cognitive model of self-rated health, suggesting that individuals primarily base their health assessments on known, consciously acknowledged diagnoses. Furthermore, this reliance on known information appears to override other “objective” health indicators, such as the intensity of health care utilization (as algorithmic detection inherently requires significant interaction with the health care system).
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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 | 0.007 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".