A Community Care Model of Intravenous Antibiotic Therapy for Injection Drug Users with Deep Tissue Infection for “Reduce Leaving Against Medical Advice”
Bibliographic record
Abstract
Deep tissue infection is a serious sequela that often demands intravenous (IV) antibiotic treatment. With respect to IV drug users (IDU's), research and lived experience demonstrates a trend of failed treatment outcomes, most notably associated with leaving hospital against medical advice (LAMA) prior to treatment completion, increased adverse outcomes and patient hardship. This paper examines an alternative model for delivering and completing IV antibiotic treatment to IDU's in a community care setting. A retrospective study was designed to review client characteristics. A total of 33 in-depth interviews were conducted with clients, clinicians and with staff. The impact of treatment adherence and completion, as well as client satisfaction of care was explored. A total of 165 patients were admitted during the study period. Osteomyelitis was the primary cause for IV antibiotics. Risk of leaving AMA was significantly lower for community model (p value <0.0000). Qualitative narrative analysis is also described with respect to satisfaction, stigma and the need for better models of care. With lower rates of LAMA a community model ought to be considered on a wider scale for provision of comprehensive support for populations with complex underlying health needs.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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, 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".