Perceived impact of COVID-19 lockdown on access to healthcare services, food affordability and family income among married patients of a resource-limited primary care setting
Bibliographic record
Abstract
Objective: To examine the perceived impact of COVID-19 lockdown on access to healthcare services, food affordability, and family income during the first phase of the pandemic among married people of a resource-limited primary care setting in Kano, Nigeria. Methods: This survey involved 432 married respondents systematically selected from attendees of a primary care clinic in Kano, Nigeria, using a structured questionnaire. Results: Over half (53.5%) of respondents or their family members fell ill during the lockdown; 67.1% felt it difficult to access hospital treatment, while 32.9% sick patients resorted to self-medication. Over half (57.2%) could afford food as they used to, 75.0% reported that food items were costly, while 35.9% received government assistance. Only 29.9% had employment, of which 49.6% received a salary, while 29.5% had their salaries reduced. Educational level was significantly associated with ease of accessing healthcare services (χ2 =8.528, P=0.014). Age (χ2=12.209, P<0.001), family type (χ2 =12.943, P<0.001), home location in Kano state (χ2= 15.397, P<0.001) and family headcount (χ2=3.968, P=0.044) were significantly associated with perceived food affordability. Conclusions: This study demonstrated the negative impact of the lockdown on healthcare access, food affordability, and family income among respondents’ families. This suggests the need for more investments in promoting and scaling up telemedicine services as platforms for accessing healthcare, which could be utilized in similar future events. More studies will be needed to ascertain the complete picture of the implementation of social assistance in the study area to enhance planning and distribution of future government social assistance.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".