Communication in disasters to support families with children with medical complexity and special healthcare needs: a rapid scoping review
Bibliographic record
Abstract
Disasters can disrupt normal healthcare processes, with serious effects on children who depend upon regular access to the health care system. Children with medical complexity (CMC) are especially at risk. These children have chronic medical conditions, and may depend on medical technology, like feeding tubes. Without clear, evidence-based processes to connect with healthcare teams, families may struggle to access the services and supports they need during disasters. There is limited research about this topic, which has been pushed forward in importance as a result of the COVID-19 pandemic. The authors therefore conducted a rapid scoping review on this topic, with the intention to inform policy processes. Both the peer-reviewed and gray literatures on disaster, CMC, and communication were searched in summer 2020 and spring 2021. Twenty six relevant articles were identified, from which four main themes were extracted: 1. Cooperative and collaborative planning. 2. Proactive outreach, engagement, and response. 3. Use of existing social networks to connect with families. 4. Return to usual routines. Based on this review, good practices appear to involve including families, professionals, other stakeholders, and children themselves in pre-disaster planning; service providers using proactive outreach at the outset of a crisis event; working with existing peer and neighborhood networks for support; employing multiple and two-way communication channels, including social media, to connect with families; re-establishing care processes as soon as possible, which may include virtual connections; addressing mental health issues as well as physical functioning; and prioritizing the resumption of daily routines. Above all, a well-established and ongoing relationship among children, their caregivers, and healthcare teams could reduce disruptions when disaster strikes.
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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 | 0.012 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".