Evidence map›Paper›PMID 37456023›Full record

ArticleHeliyon2023

Prolonged school closure during the pandemic time in successive waves of COVID-19- vulnerability of children to sexual abuses - A case study in Tamil Nadu, India.

Kandaswamy Paramasivan, Bhiksha Raj, Nandan Sudarasanam, Rahul Subburaj

Open access · goldAbstract read
In one paragraph

Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.9field-weighted citation impact, top 25% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 3 countries.

Kandaswamy ParamasivanDepartment of Management Studies, Indian Institute of Technology, Madras @ Chennai, India.
Bhiksha RajSchool of Computer Science, Carnegie Mellon University, Pittsburgh, USA.
Nandan SudarasanamDepartment of Management Studies, Indian Institute of Technology, Madras @ Chennai, India.
Rahul SubburajSenior Data Scientist, Ford Motor Company, Chennai, India.
Indian Institute of Technology Madras · INCarnegie Mellon University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: The Tamil Nadu government mandated several stay-at-home orders, with restrictions of varying intensities, to contain the first two waves of the COVID-19 pandemic. This research investigates how such orders impacted child sexual abuse (CSA) by using counterfactual prediction to compare CSA statistics with those of other crimes. After adjusting for mobility, we investigate the relationship between situational factors and recorded levels of cases registered under the Protection of Children from Sexual Offences Act (POCSO). The situational factors include the victims' living environment, their access to relief agencies, and the competence and responsiveness of the police. Methods: We adopt an auto-regressive neural network method to make a counterfactual forecast of CSA cases that represents a scenario without stay-at-home orders, relying on the eight-year daily count data of POCSO cases in Tamil Nadu. Using the insights from Google's COVID-19 Community Mobility Reports, we measure changes in mobility across various community spaces during the various phases of stay-at-home orders in both waves in 2020 and 2021. Results: The steep falls in POCSO cases during strict stay-at-home periods, compared with the counterfactual estimates, were -72% (Cliff's delta -0.99) and -36% (Cliff's delta -0.65) during the first and second waves, respectively. However, in the post-lockdown phases, there were sharp increases of 68% (Cliff's delta 0.65) and 36% (Cliff's delta 0.56) in CSA cases during the first and second waves, with concomitantly quicker reporting of case registration. Conclusions: Considering that the median delay in filing CSA complaints was above 30 days in the mild and post-intervention periods, the upsurge of cases in the more relaxed phases indicates increased occurrences of CSA during strict lockdowns. Overall, higher victimization numbers were observed during the prolonged lockdown-induced school closures. Our findings highlight the time gap between the incidents and their registration during the strict lockdown phases.

Indexed as

Auto-regressive recurrent neural networkChild abuseCounterfactual predictionPandemicSexual abuse of children

Identifiers

PMID37456023
PMCPMC10339019
OpenAlexW4382651850

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.