Navigating Challenges and Leveraging Technology: Experiences of Child Welfare Workers during the COVID-19 Pandemic
Bibliographic record
Abstract
This qualitative study explores the experiences of child welfare workers during the COVID-19 pandemic through virtual interviews, focusing on the challenges and adaptations in their work and support systems. Participants reported significant difficulties in maintaining a healthy work–life balance, heightened stress, anxiety, and increased workloads due to sick leaves and burnout. This study highlights the dual role of technology as both a stressor and a crucial tool, with rapid integration posing challenges while also enabling continued support for children and families. Despite these challenges, workers demonstrated resilience and creativity, developing innovative solutions to navigate the new landscape. The findings underscore the importance of robust support systems, clear communication, and equitable access to technology. This study suggests integrating lessons learned during the pandemic into future child welfare practices to enhance resilience and adaptability in the face of future crises.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.014 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| 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".