Bibliographic record
Abstract
BACKGROUND: Users who access needle exchanges are sometimes recruited to act as secondary distributors in an effort to reach a broader range of individuals who inject drugs. Although evaluations have demonstrated the efficiency of such approaches, more recent research has begun to uncover particular challenges associated with assuming these intermediary roles. METHODS: This article provides insights drawn from four focus-group sessions with 17 volunteers, termed natural helpers, who have between 1 and 14 years experience acting as secondary distributors for an Atlantic Canadian needle exchange. RESULTS: From the perspective of the natural helpers involved in this research, medical professionals consider those who inject drugs to be undeserving of the care accorded to more "responsible" patients. As a consequence of such disenfranchisement, natural helpers find themselves drawn into many forms of informal "doctoring" that extend far beyond their official roles as secondary distribution agents. In addition to providing syringes, training new users in safe injection procedures and promoting the use of sterile equipment, natural helpers try to dissuade people from starting to inject, act as first responders for overdoses, test drug potency, administer first aid, share prescription drugs such as antibiotics, offer temporary housing, counsel on emotional/psychological issues, and support those who are striving to reduce their drug consumption. CONCLUSION: The practices that have arisen in response to the distancing from professional health care experienced by those who inject drugs pose serious dilemmas and risks for not only users and natural helpers but also the general public.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.018 | 0.014 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".