Paediatric pandemic planning: children’s perspectives and recommendations
Bibliographic record
Abstract
Children, as major stakeholders in paediatric hospitals, have remained absent from discussions on important healthcare issues. One critical area where children's voices have been minimised is in the planning for future pandemics. This paper presents a subset of data from a programme of research which examined various stakeholder experiences of the severe acute respiratory syndrome (SARS) outbreaks of 2003. These data also generated recommendations for future pandemic planning. Specifically, this paper will examine the perspectives and recommendations of children hospitalised during SARS in a large paediatric hospital in Canada. Twenty-one (n = 21) child and adolescent participants were interviewed from a variety of medical areas including cardiac (n = 2), critical care (n = 2), organ transplant (n = 4), respiratory medicine (n = 8) and infectious diseases (patients diagnosed with suspected or probable SARS; n = 5). Data analyses exposed a range of children's experiences associated with the outbreaks as well as recommendations for future pandemic planning. Key recommendations included specific policies and guidelines concerning psychosocial care, infection control, communication strategies and the management of various resources. This paper is guided by a conceptual framework comprised of theories from child development and literature on children's rights. The authors call for greater youth participation in healthcare decision-making and pandemic planning.
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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 | 0.018 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 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".