Initiation Stories: An Examination of the Narratives of People Who Assist With a First Injection
Bibliographic record
Abstract
BACKGROUND: Research in the area of initiation to injection drug use that focuses on the perspective of initiators, or those who help with a first injection, is rare. OBJECTIVE: To explore the process of initiation to injection drug use from the point of view of initiators. METHODS: Semi-structured, in-depth qualitative interviews were conducted at a harm reduction program in Toronto, Canada. Twenty participants who had injected drugs in the last 30 days and who reported ever having initiated another person to injection drug use were recruited. A narrative analytic approach was used to explore the spectrum of narratives surrounding their experiences initiating others to injection drug use. RESULTS: Initiation events arise in a complex interplay of individual circumstances and social contexts. People who inject may assist with a first injection for a variety of reasons, from conceding to social pressure, to wanting to help reduce a perceived risk of harm, to assisting because it provides a sense of pride at possessing a skill or of having helped someone achieve a desired state, to assisting to obtain drugs or to cope with withdrawal, or a mix of several of these reasons at once. CONCLUSIONS/IMPORTANCE: Narratives reveal that preventing all instances of initiation is unrealistic. Combining elements from existing interventions that focus on enhancing reluctance to assist with initiation with safer injection training has the potential to reduce initiations and perhaps reduce injection related harm for novices if initiation occurs.
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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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".