MétaCan
Menu
Back to cohort
Record W2102504013 · doi:10.1186/1477-7517-10-20

Task shifting redefined: removing social and structural barriers to improve delivery of HIV services for people who inject drugs

2013· editorial· en· W2102504013 on OpenAlexaff
Lianping Ti, Thomas Kerr

Bibliographic record

VenueHarm Reduction Journal · 2013
Typeeditorial
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsUniversity of British ColumbiaSt. Paul's Hospital
FundersNational Institute on Drug Abuse
KeywordsCriminalizationPublic healthStigma (botany)MedicineHealth psychologySocial stigmaPopulationSocial issuesCriminologyHuman immunodeficiency virus (HIV)PsychiatryPublic relationsEnvironmental healthPsychologyPolitical scienceFamily medicineNursing

Abstract

fetched live from OpenAlex

HIV infection among people who inject drugs (IDU) remains a major global public health challenge. However, among IDU, access to essential HIV-related services remains unacceptably low, especially in settings where stigma, discrimination, and criminalization exist. These ongoing problems account for a significant amount of preventable morbidity and mortality within this population, and indicate the need for novel approaches to HIV program delivery for IDU. Task shifting is a concept that has been applied successfully in African settings as a way to address health worker shortages. However, to date, this concept has not been applied as a means of addressing the social and structural barriers to HIV prevention and treatment experienced by IDU. Given the growing evidence demonstrating the effectiveness of IDU-run programs in increasing access to healthcare, the time has come to extend the notion of task shifting and apply it in settings where stigma, discrimination, and criminalization continue to pose significant barriers to HIV program access for IDU. By involving IDU more directly in the delivery of HIV programs, task shifting may serve to foster a new era in the response to HIV/AIDS among IDU.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.575
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.013
GPT teacher head0.304
Teacher spread0.291 · 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.

Study designNot applicable
Domainnot available
GenreEditorial

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

Citations21
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueHarm Reduction JournalSame topicHIV, Drug Use, Sexual RiskFrench-language works237,207