MétaCan
Menu
Back to cohort
Record W4288720490 · doi:10.35502/jcswb.249

Police-mediated legal and social assistance to people who use drugs in two districts in Hanoi, Vietnam

2022· article· en· W4288720490 on OpenAlexvenueno aff
Edna Oppenheimer, Thuong Nong, Oanh Khuat, Trang Nguyen, Xuan Do, Viet Van Pham

Bibliographic record

VenueJournal of Community Safety and Well-Being · 2022
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsnot available
FundersSubstance Abuse and Mental Health Services Administration
KeywordsLaw enforcementBusinessSocial WelfareEnforcementMental healthMethadoneSocial supportRehabilitationWelfarePilot programMedicinePublic relationsNursingPolitical sciencePsychiatryMedical educationPsychologyLaw

Abstract

fetched live from OpenAlex

The Police Mediated Legal and Social Assistance pilot program was piloted in two districts in Hanoi, Vietnam, between 2018 and 2020. It aimed to strengthen the collaboration between law enforcement and the health and labour sectors and to strengthen the capacity of community services to divert people who use drugs from compulsory residential treatment and support them in accessing medical, social, and legal services in their communities. A total of 204 drug users were referred to treatment and support services by the pilot. Of them, 97 (47%) were referred to methadone clinics and 90 (44%) to recovery support programs, including home-based detoxification and voluntary rehabilitation at state-owned centres. Clients were also assisted in accessing ancillary treatment as needed, such as HIV, tuberculosis, hepatitis, mental health, vocational training, legal aid, and social support. Data indicated a high level of satisfaction from all those involved in the pilot. This pilot is the first collaboration aiming to assist drug users, between law enforcement, community organizations, and the health and welfare sectors. It represents a major shift in the evolving drug policy of Vietnam.

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.503
Threshold uncertainty score0.967

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.020
GPT teacher head0.315
Teacher spread0.295 · 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 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

Citations2
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Community Safety and Well-BeingSame topicHIV, Drug Use, Sexual RiskFrench-language works237,207