106 Understanding the Impact of COVID-19 on Healthcare for Medically-Complex Children and Youth
Bibliographic record
Abstract
Abstract Primary Subject area Complex Care Background COVID-19 and associated pandemic measures have disproportionately affected already vulnerable populations, including medically-complex children and youth. In Canada, about one percent of children and youth aged 0 to 18 years (inclusive) are medically complex, which is characterized by having complex, chronic conditions that require specialized care, high healthcare service usage, and functional dependence. In addition to being high users of formal healthcare services, it is estimated that parents spent an average of 52 hours per week providing unpaid care. Objectives As part of a larger study exploring the effect of the pandemic on these children and their families, the impact on healthcare usage by this population was investigated. Design/Methods In August 2020, a web-based cross-sectional survey was conducted with parents of medically-complex children and youth in British Columbia, Canada. A convenience sample was recruited through posting advertisements on social media platforms, word of mouth, and amplifying the study via the media. The survey, co-created with parent co-researchers, was comprised of 93 questions. It was divided into three sections that focused on pre- and post-pandemic questions about a) medically complex child(ren), b) family/household/community characteristics, and c) respondent demographics. Data were analyzed using descriptive statistics. Results Results illustrate the largely negative impact of the pandemic on this population’s healthcare usage. The survey was completed by 156 parents, mainly mothers (92.3%) who reported information for 188 medically complex children and youth. The children ranged in age from 0 to 18 years, with an average age of 9.5 years, and 58.0% were boys. Between February and August 2020, 30.3% of children had visited the emergency department and the same percentage had parents who avoided taking them in circumstances where they typically would have. 36.2% of the children had been admitted to hospital during that period. The children typically saw an average of four medical specialists and during the pandemic 63.8% had a specialist appointment cancelled or postponed by the clinic. During this time, there was also a steep decline or stoppage of all allied health therapies. Conclusion These results demonstrate a lack of pandemic preparedness to ensure continuity of services. Consequently, medically complex children and youth may be missing key interventions to address ongoing health issues and maintain functional abilities. More proactive planning and coordination are needed to ensure that future situations will not lead to lack of access or therapy for this vulnerable group.
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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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".