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The Hmong Youth Task Force: Evaluation of a Coalition to Address the Sexual Exploitation of Young Runaways

2007· article· en· W2067317032 on OpenAlexaff
Elizabeth Saewyc, Windy Solsvig, Laurel Edinburgh

Bibliographic record

VenuePublic Health Nursing · 2007
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsCommunity Based Research CentreUniversity of British Columbia
Fundersnot available
KeywordsTask forceTask (project management)Sexual behaviorCriminologySexual abusePsychologyPoison controlHuman factors and ergonomicsMedicinePolitical scienceSocial psychologyMedical emergencyEngineering

Abstract

fetched live from OpenAlex

The Minnesota Wheel of Public Health Nursing Interventions identifies coalition building and community organizing as effective strategies for addressing population health issues. One program that exemplifies these strategies is the Hmong Youth Task Force, a coalition formed to address a growing issue of young Hmong girls in a Midwest state running away from home, being truant from school, and experiencing subsequent sexual exploitation. This is an evaluation of the Task Force. It draws on existing records and semi-structured interviews with Task Force members from various sectors of government, health services, and community organizations, including public health nurses. The results, evaluated in the context of best practices identified by the Wheel of Interventions, document the Task Force's development, accomplishments, challenges faced, and community changes that have resulted from the coalition's efforts to date.

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)0.056
metaresearch head score (Gemma)0.043
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.174
Threshold uncertainty score0.346

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0560.043
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0060.002
Scholarly communication0.0020.002
Open science0.0020.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.409
GPT teacher head0.525
Teacher spread0.116 · 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 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

Citations24
Published2007
Admission routes1
Has abstractyes

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