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Record W4381252606 · doi:10.1080/23322705.2023.2219224

Challenges to Supporting Domestically Sex Trafficked Persons: In-Depth Interviews with Service Providers

2023· article· en· W4381252606 on OpenAlexaffabout
Frances Recknor, Robín Masón, Danielle Jacobson, C. Emma Kelly, Frances Montemurro, Rhonelle Bruder, Janice Du Mont

Bibliographic record

VenueJournal of Human Trafficking · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicSex work and related issues
Canadian institutionsPublic Health OntarioUniversity of TorontoWomen's College Hospital
Fundersnot available
KeywordsService providerPublic relationsMental healthSocial workEnforcementBusinessLaw enforcementWork (physics)Sex workService (business)NursingPolitical sciencePsychologyMedicineMarketingPsychiatryFamily medicineLawEngineering

Abstract

fetched live from OpenAlex

Domestic sex trafficking is an emergent area of study with problematic gaps in our understanding of the challenges that inhibit client recovery. As social service providers are often on the frontlines of care provision, in this study, we explored the challenges they experienced when serving domestically sex trafficked adolescents and adults. Semi-structured interviews were conducted with 15 providers in Ontario, Canada’s largest province, and thematically analyzed. Our study found that providers faced systemic-, provider-, and client-related challenges, including insufficient funding, a dearth of (appropriate) shelter and/or housing, problems with healthcare and health professionals, entrenched biases within law enforcement, the weight of emotional work, fear for themselves and their clients, survivors’ misgivings about the systems established to assist them, and their unresolved concurrent mental health issues. By exploring intersections among various challenges facing service providers with the goal of improving services for domestically sex trafficked persons in Canada, we contribute to discourses informing research, policy, and practice considerations in various jurisdictions, working toward achieving UN Sustainable Development Goals 5 and 16 (specifically targets 5.2, 16.1, and 16.2).

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)0.003
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 score0.067
Threshold uncertainty score0.557

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.070
GPT teacher head0.368
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 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
Published2023
Admission routes2
Has abstractyes

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