“There is a strangeness in this disease”: A qualitative study of parents’ experiences caring for a child diagnosed with COVID-19
Bibliographic record
Abstract
BACKGROUND: The beginning of the COVID-19 pandemic marked a period of uncertainty as public health guidelines, diagnostic criteria, and testing protocols or procedures have continuously evolved. Despite the virus being declared a worldwide pandemic, little research has been done to understand how parents manage caring for their child diagnosed with COVID-19. We sought to understand parents' experiences and information need when caring for a child diagnosed with COVID-19. METHODS: A qualitative descriptive study with an inductive and exploratory approach was completed. Participants were recruited through social media and local public health clinics. Data collection and analysis were concurrent. Semi-structured virtual interviews were conducted with 27 participants. Thematic analysis was conducted. FINDINGS: Four major themes emerged: a) dealing with uncertainty; b) anxiety; c) social stigma and stress; d) a sense of community. CONCLUSION: Our study highlights that parent experiences were diverse and multi-faceted, and their experiences evolved and shifted over the course of the pandemic. Parents would benefit from clear and consistent evidence-based online information. Understanding the perspectives of parents caring for a child with COVID-19 is an important step in developing future resources tailored to meet their unique experiences and information needs.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".