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Record W4380988461 · doi:10.2196/44760

Online Help-Seeking Among Youth Victims of Sexual Violence Before and During COVID-19 (2016-2021): Analysis of Hotline Use Trends

2023· article· en· W4380988461 on OpenAlexvenueno aff
Kimberly L. Goodman, Kristyn Kamke, Tara M. Mullin

Bibliographic record

VenueJMIR Public Health and Surveillance · 2023
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsnot available
Fundersnot available
KeywordsHotlinePandemicMental healthSuicide preventionMedicinePoison controlSexual abuseYoung adultCoronavirus disease 2019 (COVID-19)PsychologyPsychiatryDemographyMedical emergencyGerontologySociology

Abstract

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BACKGROUND: Three years since the onset of COVID-19, pandemic-related trends in child sexual abuse (CSA) remain poorly understood. Common administrative surveillance metrics may have underestimated abuse during the pandemic, given youths' limited access to mandatory reporters. Research using anonymous service-use data showed increased violence-related online help-seeking but overlooked youth-specific help-seeking for CSA during COVID-19. Understanding pandemic-related trends in CSA can inform abuse detection practices and mental health service provision for youth victims. OBJECTIVE: The purpose of this study was to harness anonymous help-seeking data from the National Sexual Assault Online Hotline (NSAOH) to glean insights about CSA occurrence in the United States during the COVID-19 pandemic. METHODS: We used an archival sample of victims who contacted NSAOH from 2016 to 2021 (n=41,561). We examined differences in the proportion of youth and adult victims contacting NSAOH during the first COVID-19 year (March 2020 to February 2021) compared to the prior year (March 2019 to February 2020; n=11,719). Further, we compared key characteristics of hotline interactions among youth victims during the first COVID-19 year to the prior year (n=5913). Using joinpoint regression analysis, we examined linear trends in the number of monthly sampled youth and adult victims (excluding victims of unknown age) from 2016 to 2021 who discussed any victimization event (n=26,904) and who discussed recent events (ie, events occurring during the pandemic; n=9932). RESULTS: Most youth victims were abused by family members prior to (1013/1677, 60.4%) and after (2658/3661, 72.6%) the onset of COVID-19. The number of youth victims contacting NSAOH spiked in March 2020 and peaked in November 2020 for all youth (slope=28.2, 95% CI 18.7-37.7) and those discussing recent events (slope=17.4, 95% CI 11.1-23.6). We observed a decline in youth victims into spring 2021 for all youth (slope=-56.9, 95% CI -91.4 to -22.3) and those discussing recent events (slope=-33.7, 95% 47.3 to -20.0). The number of adult victims discussing any victimization event increased steadily from January 2018 through May 2021 (slope=3.6; 95% CI 2.9-4.2) and then declined (slope=-13.8, 95% CI -22.8 to -4.7). Trends were stable for adults discussing recent events. CONCLUSIONS: This study extends the use of hotline data to understand the implications of the pandemic on CSA. We observed increased youth help-seeking through the NSAOH coinciding with the onset of COVID-19. Trends persisted when limiting analyses to recent victimization events, suggesting increased help-seeking reflected increased CSA during COVID-19. These findings underscore the utility of anonymous online services for youth currently experiencing abuse. Further, the findings support calls for increased youth mental health services and efforts to incorporate online chat into youth-targeted services.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.048
GPT teacher head0.346
Teacher spread0.298 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations4
Published2023
Admission routes1
Has abstractyes

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