Pandemic checkups: Mobile paediatric care and vaccination in disadvantaged areas
Bibliographic record
Abstract
In 2019, 10.1% of Canadians lived below the national poverty line (1). This percentage is greater among immigrants who arrived in Canada within the last 10 years (17.4%) (1). Poverty is a health issue: lower income is associated with poorer health outcomes, such as shorter life expectancy, mental health disorders, diabetes, and cardiovascular comorbidities (2). Children experiencing poverty are at higher risk of infant and child mortality and morbidity due to injury, asthma and developmental delay (2). In addition to having increased risk of adverse health outcomes, low-income, racially diverse populations face linguistic, structural, and psychological obstacles in accessing health care (2,3). These marginalized groups have also reported challenges in establishing trusting relationships with health care providers (4). COVID-19 has exacerbated these health inequities, as public health measures led to a reduction in health care appointments conducted in person. Reorganization of services led to a deprioritization of elective clinical activity, such as well-child visits (5). Furthermore, families were hesitant to seek care due to fear of contracting COVID-19 (6). To palliate these gaps in care, telemedicine has been widely adopted during the pandemic (7). Despite telemedicine improving health care access for some patients, vulnerable families have unequal access to virtual care (7).
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.055 | 0.004 |
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".