Effective child well-being practices, barriers and priority actions: survey findings from service providers and policymakers in 22 countries during COVID-19
Bibliographic record
Abstract
Purpose The purpose of this study was to explore 232 service providers’ and policymakers’ experiences of supporting children’s well-being during the pandemic, across sectors, in 22 countries – including Kenya, the Philippines, South Africa, India, Scotland, Sweden, Canada and the USA, in the last quarter of 2020. Design/methodology/approach A smartphone survey delivered via a custom-built app containing mostly open-ended questions was used. Respondents were recruited via professional networks, newsletters and social media. Qualitative content analysis was used. Findings The findings reveal numerous system-level challenges to supporting children’s well-being, particularly virus containment measures, resource deficiencies and inadequate governance and stakeholder coordination. Those challenges compounded preexisting inequalities and poorly affected the quality, effectiveness and reach of services. As a result, children’s rights to an adequate standard of living; protection from violence; education; play; and right to be heard were impinged upon. Concurrently, the findings illustrate a range of adaptive and innovative practices in humanitarian and subsistence support; child protection; capacity-building; advocacy; digitalisation; and psychosocial and educational support. Respondents identified several priority areas – increasing service capacity and equity; expanding technology use; mobilising cross-sectoral partnerships; involving children in decision-making; and ensuring more effective child protection mechanisms. Practical implications This study seeks to inform resilience-enabling policies and practices that foster equity, child and community empowerment and organisational resilience and innovation, particularly in anticipation of future crises. Originality/value Using a novel approach to gather in-the-moment insights remotely, this study offers a unique international and multi-sectoral perspective, particularly from low- and middle-income countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".