Counting Pills or Counting on Pills? What HIV+ Women Have to Say About Antiretroviral Therapy
Bibliographic record
Abstract
Despite the obvious contextual complexity of women and HIV/AIDS their lived experiences continue to be all but ignored in most HIV research prevention programs and medication compliance campaigns. In order to address the broad factors that enhance women’s risk for HIV infection and consequently women’s access to and utilization of antiretroviral medications to prevent the disease progression the complex social cultural and economic reality of women’s lives must be considered. This article outlines the lived experiences of a sample of Ontario women living with HIV. The purpose of this study is to explore women’s perceptions of medication adherence the factors that contribute to non-adherence and to offer suggestions for programmatic changes in current practice to better meet the needs of women living with HIV (Gahagan). Due to the overall lack of pre-existing research focusing specifically on issues faced by HIV positive women a grounded theory approached was used to examine the issue of adherence. In grounded theory interviewing and data analysis are closely connected and take place throughout the research process in a cyclical rather than linear manner (Strauss and Corbin). (excerpt)
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".