Pediatric epidemic crisis: Lessons for policy and practice development
Bibliographic record
Abstract
Objectives: This research study addresses health policy and patient care considerations, and outlines policy and practice implications resulting from a crisis in a pediatric setting. This crisis, an epidemic outbreak of Severe Acute Respiratory Syndrome (SARS), dramatically impacted the delivery of health care in Canada. Despite the passage of time since the last diagnosed case of SARS in April 2004, researchers have warned the global community to be prepared for future outbreaks of SARS or other infectious diseases. Methods: Qualitative interviews were conducted with 23 participants representing key stakeholder groups: (a) pediatric patients with probable or suspected SARS, (b) their parents, and (c) health care professionals providing direct care to SARS patients. Results: Participants conveyed key areas in which health policy and practice were affected. These included the development of communication strategies for responding to SARS easing vulnerability among all stakeholders; and the rapid development of practice guidelines. Conclusion: Given the continuing threat of current and future airborne viruses with potential for epidemic spread and devastating outcomes, preparedness strategies are certainly needed. Effective strategies in pediatrics include practices that provide family centered care while minimizing disease transmission. Toward this end, lessons learned from previous outbreaks merit consideration and may inform future epidemics.
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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 | 0.046 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.020 | 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".