Association between chronic conditions and perceived unmet health care needs.
Bibliographic record
Abstract
BACKGROUND: Although effective treatments exist, many Canadians with chronic medical conditions do not receive the full care they require, possibly as a consequence of limited accessibility or availability. A commonly used indicator of inadequate access to or availability of care is the perception of unmet health care needs. The objective of this study was therefore to determine the association between chronic conditions and perceived unmet health care needs. METHODS: We extracted data for adult respondents from the combined 2001, 2003 and 2005 cross-sectional cycles of the Canadian Community Health Survey. Multivariate logistic regression was used to estimate the association between 7 high-prevalence and high-impact chronic conditions (arthritis, chronic obstructive pulmonary disease/emphysema, diabetes, heart disease, hypertension, mood disorder and stroke) and perceived unmet health care needs in the prior 12 months, adjusting for sociodemographic variables, health behaviours, health status and survey cycle. RESULTS: Of the 360 105 adult respondents, 12.2% reported an unmet health care need. Compared with those without chronic conditions, respondents with at least one condition were more likely to report an unmet need (adjusted odds ratio [OR] 1.51, 95% confidence interval [CI] 1.45-1.59). Those with mood disorders were almost twice as likely to report an unmet need (OR 1.94, 95% CI 1.78-2.12), while those with diabetes or hypertension were less likely to report an unmet need (diabetes OR 0.85, 95% CI 0.76-0.94; hypertension OR 0.96, 95% CI 0.89-1.04). Furthermore, the likelihood of an unmet need increased with the number of chronic conditions (OR 1.71, 95% CI 1.56-1.88 for 3 or more conditions). Respondents with chronic conditions were more likely than those without to report an unmet need related to resource availability (OR 1.14, 95% CI 1.06-1.22). INTERPRETATION: Adults with chronic medical conditions are more likely to report an unmet health care need, and the likelihood increases with an increasing number of conditions. Whether these unmet needs are associated with worse outcomes, and whether interventions targeted to address these needs may improve outcomes for Canadians with chronic disease, remain to be determined.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".