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Record W1584617658 · doi:10.1016/s2214-109x(15)00031-5

Reducing school violence in Africa: learning from Uganda

2015· letter· en· W1584617658 on OpenAlexaff
William Pickett, Frank J. Elgar

Bibliographic record

VenueThe Lancet Global Health · 2015
Typeletter
Languageen
FieldHealth Professions
TopicChild and Adolescent Health
Canadian institutionsMcGill UniversityDouglas Mental Health University InstituteQueen's University
Fundersnot available
KeywordsScopusConvention on the Rights of the ChildMedicineCluster randomised controlled trialHuman rightsFamily medicineIntervention (counseling)Medical educationNursingPolitical scienceMEDLINELaw

Abstract

fetched live from OpenAlex

Violence pervades the lives of children around the world. For too long, society has ignored child violence and failed to hold adult guardians to account for their traumatising actions towards children. The right to be protected from violence is guaranteed by the United Nations Convention on the Rights of the Child,1United NationsConvention on the rights of the child. United Nations, New York1989http://www.ohchr.org/EN/ProfessionalInterest/Pages/CRC.aspxGoogle Scholar and yet children in many countries are routinely exposed to physical attacks as victims or as bystanders. Moreover, even though children spend more time in school than in any other setting, robust evidence on the prevention of violence in schools outside North America is scarce. The community trial by Karen Devries and colleagues in The Lancet Global Health2Devries KM Knight L Child JC et al.The Good School Toolkit for reducing physical violence from school staff to primary school students: a cluster-randomised controlled trial in Uganda.Lancet Glob Health. 2015; 3: e378-e386Summary Full Text Full Text PDF Scopus (116) Google Scholar is therefore a bold and important initiative in the field of paediatric violence. The trial evaluated a complex behavioural intervention—the Good School Toolkit, designed by non-profit organisation Raising Voices—in 42 Ugandan schools.2Devries KM Knight L Child JC et al.The Good School Toolkit for reducing physical violence from school staff to primary school students: a cluster-randomised controlled trial in Uganda.Lancet Glob Health. 2015; 3: e378-e386Summary Full Text Full Text PDF Scopus (116) Google Scholar The need for such an intervention is stark. According to a recent UNICEF report on violence,3United Nations Children's FundHidden in plain sight: a statistical analysis of violence against children. UNICEF, New York2014http://www.unicef.org/publications/index_74865.htmlGoogle Scholar Uganda's child homicide rate is 10 per 100 000 annually—one of the highest in the world—with 36% of 13–15-year-olds having been in a physical fight during the past year and 54% of 15–19-year-olds having experienced physical violence since age 15 years. Anecdotal reports suggest that most students have experienced physical punishment at school at the hands of school staff, including caning and slapping.4Devries KM Allen E Child JC et al.The Good Schools Toolkit to prevent violence against children in Ugandan primary schools: study protocol for a cluster randomised controlled trial.Trials. 2013; 14: 232Crossref PubMed Scopus (34) Google Scholar Such experiences are shared equally between boys and girls and track strongly into adult life in experiences involving forced sexual acts and attitudes towards intimate partner violence and using physical discipline with children.3United Nations Children's FundHidden in plain sight: a statistical analysis of violence against children. UNICEF, New York2014http://www.unicef.org/publications/index_74865.htmlGoogle Scholar This randomised trial of the Good School Toolkit is important not only because of its aim—to reduce physical violence from school staff enacted on primary school children—but also because of its novelty and quality. It represents one of the few cluster-randomised controlled trials of its kind in any setting. Its objectives, study population, and methods were clear and transparent. Devries and colleagues carefully considered threats to both internal and external validity within their design and interpretation, as well as the implications of the trial findings for public health. The reach of the intervention and cooperation of the school communities, staff members, and student bodies were both excellent. Methods of follow-up and assessment conformed to the highest possible standards. Indeed, the study represents a model in terms of the conduct of a community-based trial in a school-based setting and sets a new standard for evidence in support of school-based interventions. Still, despite its impressive findings—a significantly lower rate of violence was reported in intervention schools relative to controls after 18 months (595/1921 [31·0%] vs 924/1899 [48·7%]; odds ratio 0·40, 95% CI 0·26–0·64, p<0·0001), with no apparent adverse effects of the intervention—an astute reader will observe that the total efficacy of the intervention is modest. Even after this rigorous school-based intervention, almost a third of primary school children in the intervention group of the trial still reported one or more episodes of physical violence in the past week. This is violence perpetrated by school staff—acts that in other jurisdictions and countries could lead to severe reprimands, dismissal, or even incarceration. 434 children were referred to child protective services over the course of the trial, representing one in nine trial participants. Another caveat is that, although the efficacy of the intervention is clear, its broader and long-term effects on acts of corporal punishment and other forms of violence within and outside of the school system remain unknown. Hopefully, further follow-up will show a sustained decline in reported physical violence in all settings among students assigned to the intervention, but this remains to be seen. Violence against children represents a quiet epidemic, and schools offer researchers a natural laboratory in which to measure and study its prevalence and many consequences for mental and physical health and academic outcomes. However, schools are just one context in which children are victimised. Interventions that are conducted over a short-term period might affect school cultures and experiences, but both their immediate and sustained impacts on violence in homes, workplaces, and neighborhoods remain uncertain and need further study. It is important to recognise that social and structural determinants of violence—poverty, gender discrimination and racism, socioeconomic inequalities, political unrest, untreated mental health problems, addictions, and other root causes—will persist despite the best efforts of schools to counteract them. The need remains for further research that focuses more broadly on such fundamental determinants. Still, Devries and colleagues2Devries KM Knight L Child JC et al.The Good School Toolkit for reducing physical violence from school staff to primary school students: a cluster-randomised controlled trial in Uganda.Lancet Glob Health. 2015; 3: e378-e386Summary Full Text Full Text PDF Scopus (116) Google Scholar are to be commended for their very courageous and timely work. With the resources and political will needed to include such programmes in education curricula, schools in Uganda and elsewhere are ideally situated for laying the roots of broader social change towards the elimination of violence against children. Efforts to address such acts and to change societal norms are needed not only to prevent unnecessary deaths and trauma in vulnerable populations, but also to buttress the social and economic development of entire nations. We declare no competing interests. The Good School Toolkit for reducing physical violence from school staff to primary school students: a cluster-randomised controlled trial in UgandaThe Good School Toolkit is an effective intervention to reduce violence against children from school staff in Ugandan primary schools. Full-Text PDF Open Access

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.040
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.013
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.107
GPT teacher head0.416
Teacher spread0.309 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreCommentary

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".

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Citations5
Published2015
Admission routes1
Has abstractyes

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