Barriers, Inequalities, and Public Perceptions of Healthcare Access During COVID-19 in Kazakhstan: Findings from a National Cross-Sectional Survey.
Bibliographic record
Abstract
The COVID-19 pandemic significantly disrupted healthcare access worldwide, including in Kazakhstan, where healthcare disparities may have worsened. This study examines barriers to healthcare access during the pandemic and factors associated with failed access. A cross-sectional survey of 1021 adults was conducted in Kazakhstan between June and July 2021 using a multistage stratified sampling approach. Weighted analyses accounted for selection biases and non-response. Participants reported barriers to COVID-19-related care, access to healthcare services, and the mode of care received (in person, telemedicine, or not accessed). Fear of contracting COVID-19 (38.9%), hospitalization (24.7%), and isolation-related job or income loss (23.9%) were the most common barriers. Among those needing healthcare (n=753), 21.7 % failed to access services, particularly health products (31.7%), emergency care (21.6%), and inpatient medical care (20.2%). Failed access was significantly associated with chronic conditions (ARR 1.54, 95% CI 1.13–2.11), unemployment (ARR 1.70, 95% CI 1.12–2.57), and food insecurity (ARR 1.34, 95% CI 1.02–1.77). These findings highlight the need for targeted policies to ensure equitable healthcare access during public health crises, particularly for vulnerable populations.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".