Patterns of Self-Reported Unmet Healthcare Needs Among Canadians Aged 15 Years and Older in 2001: A Descriptive Cross-Sectional Analysis
Bibliographic record
Abstract
BACKGROUND: Access to timely and adequate healthcare remains a major public health concern, even in countries with universal health coverage such as Canada. Unmet healthcare needs, particularly among individuals with chronic conditions, highlight significant gaps in service delivery and equity. OBJECTIVE: This study aimed to describe the distribution and relative proportions of self-reported reasons for unmet healthcare needs among Canadians aged 15 years and older. METHODS: A descriptive cross-sectional analysis was conducted using secondary data from Statistics Canada's Open Government Portal (Table 13-10-0692, 2015). The dataset included aggregated national estimates representing a total study population of 2,546,977 individuals aged 15 years and above. Only valid percentage-based data were analyzed, excluding flagged or unreliable records. Descriptive statistics and visual summaries were used to identify the most common barriers. RESULTS: Among the study population (n=2,546,977), 36 valid aggregated observations, long waiting times (n=1,044,318; 49.4%), and service unavailability (n=773,451; 36.6%) were the leading reasons for unmet healthcare needs. Other reported barriers included perceived inadequacy of care (n=157,714; 7.5%) and personal constraints such as being too busy (n=77,600; 3.7%). CONCLUSION: Prolonged waiting times and limited service availability remain the most critical barriers to healthcare access in Canada, particularly affecting individuals with chronic illnesses. These findings should be interpreted with caution, as several data points in the Statistics Canada file were flagged for limited reliability and subsequently excluded, which may narrow the completeness of the reported barriers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".