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Record W7135745963

The prevalence of child maltreatment in India and its association with gender, urbanisation and policy: a systematic review and meta-analysis protocol

2020· article· en· W7135745963 on OpenAlexaboutno aff
Gwen Fernandes

Bibliographic record

VenueBristol Research (University of Bristol) · 2020
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsnot available
Fundersnot available
KeywordsChecklistNeglectChild abusePoison controlSystematic reviewUrbanizationProtocol (science)Sexual abuseOccupational safety and health
DOInot available

Abstract

fetched live from OpenAlex

Introduction: India is home to 20% of the world’s children and yet, little is known on the magnitude and trends of child maltreatment nationwide. The aims of this systematic review are to provide a prevalence of child maltreatment in India with considerations for any effects of gender; urbanisation (e.g. urban versus rural); and, legislation (Protection of Children from Sexual Offences Act 2012). Methods and Analysis: A systematic review will be undertaken of all quantitative peer-reviewed studies on child maltreatment in India between 2005 and 2020. Four electronic databases will be systematically searched: PubMed, EMBASE, Cochrane and PsychIndex. The primary outcomes will include all aspects of child maltreatment: physical abuse, sexual abuse, emotional abuse, emotional neglect and physical neglect. Study participants will be between 0-18 years and will have reported maltreatment experiences using validated, reliable tools such as the Adverse Childhood Experiences Questionnaire as well as child self-reports and clinician reports. Methodological appraisal of the studies will be assessed by the Newcastle-Ottawa Quality assessment scale. Also, if sufficient data are available, a meta-analysis will be conducted. Effect sizes will be determined from random-effects models stratified by gender, urbanisation and pre-and post the 2012 POCSO Act cut off. Ethics and Dissemination: As this is a systematic review, minimal ethical risks are expected. A Level 1 self-audit checklist has been approved by an ethics panel in the Usher Institute at the University of Edinburgh. Findings from this review will be disseminated widely through peer-reviewed publication and in various media, for example, conferences, congresses or symposia. Registration: A study protocol was developed before starting this systematic review and was registered with the international prospective register of systematic reviews (PROSPERO) (registration number CRD42019150403)

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.060
metaresearch head score (Gemma)0.065
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.060
Threshold uncertainty score0.318

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0600.065
Meta-epidemiology (narrow)0.0060.005
Meta-epidemiology (broad)0.0240.031
Bibliometrics0.0140.011
Science and technology studies0.0030.003
Scholarly communication0.0070.006
Open science0.0060.005
Research integrity0.0070.005
Insufficient payload (model declined to judge)0.0600.005

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 head0.099
GPT teacher head0.354
Teacher spread0.255 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreProtocol

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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