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Record W4213288549 · doi:10.1080/10538712.2022.2037803

Confidence, Training and Challenges for Canadian Child Advocacy Center Staff When Working with Cases of Online and In-person Child Sexual Exploitation

2022· article· en· W4213288549 on OpenAlexafffundabout
David Lindenbach, Gina Dimitropoulos, Asmita Bhattarai, Olivia Cullen, Rosemary Perry, Paul Arnold, Scott B. Patten

Bibliographic record

VenueJournal of Child Sexual Abuse · 2022
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsUniversity of Calgary
FundersPalix Foundation
KeywordsChild sexual abuseSexual abusePsychologyPornographyChild abuseChild pornographyPoison controlSuicide preventionClinical psychologyThe InternetMedicineMedical emergency

Abstract

fetched live from OpenAlex

Child Advocacy Centers are interdisciplinary hubs that play a vital role in responding to child maltreatment, especially sexual abuse. Sexual abuse cases increasingly involve an online component, but no studies have examined the experience of Child Advocacy Center staff in dealing with online sexual exploiftation. This study surveyed 37 staff at five Child Advocacy Centers in Alberta, Canada to understand their ability to recognize and respond to concerns about online and in-person sexual exploitation of their clients. The majority of respondents (54%) dealt with cases that involved grooming, luring, sexual abuse and child sexual abuse imagery (also known as child pornography) in the last year. Staff were equally confident in their ability to recognize and respond to grooming, luring, sexual abuse and child sexual abuse imagery. However, staff were more likely to have formal training in identifying sexual abuse and less likely to encounter difficulties in responding to sexual abuse relative to grooming, luring or child sexual abuse imagery. Clinicians used similar therapies when working with youth impacted by sexual abuse versus child sexual abuse imagery. Given that most Child Advocacy Center staff in our sample dealt with online child sexual exploitation, additional training in this area may be warranted.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.185
Threshold uncertainty score0.946

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.088
GPT teacher head0.292
Teacher spread0.204 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations9
Published2022
Admission routes3
Has abstractyes

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