Optimizing adherence to highly active antiretroviral therapy among marginalized, HIV-positive women with comorbid depression: the role of the nurse practitioner
Bibliographic record
Abstract
Women represent a growing proportion of new positive HIV tests in Canada and are more likely to experience comorbid depression. This is concerning because comorbid depression is associated with suboptimal adherence to highly active antiretroviral therapy (HAART) and poor HIV disease outcomes. The purpose of this project is to answer the question, What is the role of the nurse practitioner (NP) in optimizing adherence to HAART amongst marginalized, HIV-positive women with comorbid depression? By evaluating current literature this project found that social factors such as poverty, homelessness, food insecurity, post-traumatic stress disorder, stigmatization, gender or racial discrimination and injection drug use are risk factors for both comorbid depression and HAART non-adherence. Effective treatments for depression include cognitive behavioral therapy and pharmacological intervention with antidepressants. The role of the NP was inferred by extrapolating the findings from this literature search with the competencies required of NP practice. NPs have the skills, education and legislated authority to provide collaborative care that addresses the underlying social factors that contribute to comorbid depression and HAART non-adherence. These findings are limited by the scarcity of research conducted with HIV-positive female participants, that investigates NP practice or interventions to address underlying social factors. --leaf ii.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".