Personal narrative writing workshops for medical students and patients with HIV: narrative medicine in the post-HAART era
Bibliographic record
Abstract
Since the advent of highly active antiretroviral therapy(HAART) in the mid-1990's, HIV in the United States has become a chronic and largely controllable disease. Adherence to therapy is one of the most crucial aspects of HIV treatment and control due to the high risk of viral resistance. The main barriers to successful treatment are now psychosocial and structural, including social stigma and the high burden of disease in vulnerable communities. To improve clinical outcomes, physicians today must learn to engage with their patients on the level of their lived experiences, which include their social backgrounds and personal values and priorities. In 2016, supported by a Narrative Medicine Fellowship from Columbia University, we piloted a novel narrative medicine-based medical education intervention in which patients with HIV and medical students from the Keck School of Medicine of the University of Southern California wrote and shared personal narratives with each other. Nine medical students and five patients participated in one of two five-week long workshop series. Patients were recruited from the Maternal, Child, and Adolescent/Adult Clinic at Los Angeles County General.Mixed methods were used to evaluate the feasibility and effectiveness of the intervention. This included the development of a grounded theory of participants’ experiences of the workshop series. Participants articulated how the workshop series expanded their sense of agency, humanity, and empathy toward others, enabling them to explore new ideals for therapeutic physician-patient relationships. The results of the study, as well as the workshop series method and syllabus, will be presented.
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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".