The policy- practice gap: an analysis of policies focusing on violence against children
Bibliographic record
Abstract
Abstract Background Violence against children is a pervasive global health problem, with a high prevalence of violence occurring in low-and middle-income countries (LMICs). Studies underscore how exposure to violence at an early age leads to several long-term negative health and wellbeing outcomes, including an increased risk of non-communicable diseases. However, there is a limited body of evidence evaluating the effectiveness of existing policies that focus on children experiencing violence. Therefore, our objective is to examine the extent to which policies at the international level and in selected LMICs (Tanzania, Ghana, Pakistan, Uganda, and Haiti) explicitly focus on violence against children. Methods We conducted a summative content analysis of policy documents focusing on violence against children: 10 at the international level, eight from Tanzania, five from Ghana and Pakistan, four from Uganda and two from Haiti. We also conducted in-depth interviews (n = 127) and focus groups (n = 34) with children from the countries mentioned above. Results Findings from the policy analysis have underscored that while the majority of policies aim to prevent violence against children, there is a lack of detailed information regarding context-specific social norms, accountability mechanisms, and stakeholder engagement. Around 10% of the included policies contain vague language, making their interpretation and implementation inconsistent. Results from our in-depth interviews and focus groups further highlight the gap between policies and lived realities, with the majority of children sharing experiences of violence either they or their peers had faced at home, in school or in other settings. Conclusions Overall, our research speaks to the disconnect between policy and practice when focusing on preventing violence against children in LMICs. There is a need for a community-informed approach to policy development and implementation to ensure a reduction in violence against children. Key messages • Despite numerous policies implemented worldwide to prevent violence against children, it remains a persistent global health challenge, especially in LMICs. • To prevent violence against children, policies need to involve stakeholders, have clear language and incorporate monitoring and reporting tools to track progress and ensure accountability.
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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 | 0.069 | 0.170 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| 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".