37 Accessible and Specialized COVID-19 Testing for Children and Youth with Medical Complexity
Bibliographic record
Abstract
Abstract Background COVID-19 testing for symptomatic individuals is a key public health measure for infection prevention and control. However, COVID-19 testing can be uncomfortable without appropriate supports and can lead to testing hesitancy amongst certain populations such as children with medical complexity (CMC) and those with underlying neurological and respiratory conditions. To support COVID-19 testing, a specialized initiative was developed for CMC and their families onsite at The Hospital for Sick Children to enhance testing uptake, reduce barriers to access, and support a safe and accommodated testing environment for families. Multiple modalities of testing were involved and could be completed in their personal vehicle, with specialized support from nurses and child life if needed. Objectives The objectives of our study were to investigate the characteristics of CMC and their families who underwent COVID-19 testing through our program, evaluate indications for testing, and collect case positivity rates. Design/Methods Prospective data, including testing and population characteristics, were collected from December 2020-August 2021 through a centralized system, and was analyzed using descriptive methods. Results 335 children (Table 1) with medical complexity came to the COVID-19 Assessment Center for testing. Of those who were tested 88% (294) had neurodevelopmental conditions with highly challenging behaviours (e.g. autism, developmental delay), and 12% (28) were classified as CMC (i.e. those with active use of medical technology e.g. tracheostomy, G-tube etc.). Of those tested, 6% (21) tested positive for COVID-19. Sixty percent (199) were tested due to having symptoms consistent with COVID-19, 27% (90) had a COVID-19 exposure, 8% (26) were exposed and tested as part of outbreak management and 5% were of an unknown criteria. The majority of completed tests (74%) were nasopharyngeal (NP) swabs, 18% completed saliva tests and 6% completed anterior nares/throat swab tests. Thirteen percent (43) of families requested additional supports such as extra nurses, child life specialists or other accommodations. All patients had a dedicated paediatric nurse and received testing in their personal vehicle. Conclusion CMC and their families face unique barriers to COVID-19 testing. A specialized testing centre for CMC was able to support families by providing unique opportunities for testing, revealing a 6% COVID-19 positivity rate. NP swabs that can be painful were supported through in-vehicle testing with dedicated pediatric nurses. Robust health and safety measures, including a coordinated testing approach, are necessary to ensure accessible testing opportunities for CMC and their families. Further research is needed to be able to support this unique population.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".