Task shifting redefined: removing social and structural barriers to improve delivery of HIV services for people who inject drugs
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.016 | 0.029 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".