Unmet Needs Reported by Adults with Chronic Conditions:An Analysis of Data from the Canadian Community Health Survey
Bibliographic record
Abstract
Background: Maximizing function in daily life is a primary goal for persons with chronic conditions.Persons with chronic conditions have reported moderate to severe disability in daily living and frequently use complex and costly healthcare services. Unmet rehabilitation needs can limit activities, restrict participation, cause deterioration of health, increase dependence on others and decrease quality of life. The purpose of the study is to analyze self reported unmet needs of adults with one or more of a specific list of chronic conditions who resided in Ontario, Alberta or British Columbia, Canada (the study population) using data from the Canadian Community Health Survey (CCHS) (Cycles 2001, 2003, and 2005).Methods: Public use micro data files were downloaded for each CCHS cycle. Patterns of missing data were investigated and accounted for by multivariate imputation using chained equations. The dependent variables of availability, affordability, and acceptability, (three dimensions of access to care), were derived from existing data. Descriptive analysis and logistic regressions were completed to identify relationships between each dependent variable and independent variables.Results: Unmet need for treatment of a physical health condition (physical unmet need) was the most common type of need reported by adults in the study population in three CCHS cycles. Significant associations were identified for age (> 50 years) and sex (female) with each of the dimensions of access to care.Conclusions: Physical unmet need associated with availability, affordability and acceptability of care was identified in the study population in each of the survey cycles. Physiotherapists are well positioned to address this unmet need.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".