Uncovering the wider impact of COVID-19 public health measures on the lives of children with complex care needs and their families in the Canadian Maritimes: a qualitative study
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic resulted in rapid change across all sectors, including healthcare and education, as necessary public health measures (PHM) were implemented to protect the public. Children with complex care needs (CCCN) use health and specialised education services at a higher rate than the general paediatric population and were disproportionately affected by these changes. The current study aimed to explore the wider impacts of COVID-19 PHM on CCCN and their families in the Maritime provinces of Canada. METHODS: Semi-structured interviews were conducted from March to September 2023, with CCCN aged 15-17 years old and caregivers of CCCN. Interviews were coded by two independent reviewers using the Ten Domains of Health Framework and interpreted using a phenomenological approach. RESULTS: 21 parents and 3 youth from across the three Maritime provinces participated in the interviews. Key themes that emerged from the interviews included families carrying the burden of stress and fear, creating and protecting safe spaces, living through uncertainty, accumulating consequences of PHM and feeling disconnected. Within these themes, families mostly described negative impacts, although some positive experiences were also described. CONCLUSION: The findings of the current study will assist in the co-development of recommendations for healthcare and education decision-makers to reduce the negative impacts of future public health emergencies.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.012 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".