Development of a nursing intervention to facilitate optimal antiretroviral-treatment taking among people living with HIV
Bibliographic record
Abstract
BACKGROUND: Failure by a large portion of PLHIV to take optimally ARV treatment can have serious repercussions on their health. The absence of a systematic treatment-taking promotion program in Quebec prompted stakeholders to develop jointly a theory- and evidence-based nursing intervention to this end. This article describes the results of a collective effort by researchers, clinicians and PLHIV to share their knowledge and create an appropriate intervention. METHODS: Intervention mapping was used as the framework for developing the intervention. First, the target population and environmental conditions were analyzed and a literature review conducted to identify predictors of optimal treatment taking. The predictors to emerge were self-efficacy and attitudes. Performance objectives were subsequently defined and crossed-referenced with the predictors to develop a matrix of change objectives. Then, theories of self-efficacy and persuasion (the predictors to emerge from step 1), together with practical strategies derived from these theories, were used to design the intervention. Finally, the sequence and content of the intervention activities were defined and organized, and the documentary material designed. RESULTS: The intervention involves an intensive, personalized follow-up over four direct-contact sessions, each lasting 45-75 minutes. Individuals are engaged in a learning process that leads to the development of skills to motivate themselves to follow the therapeutic plan properly, to overcome situations that make taking the antiretroviral medication difficult, to cope with side-effects, to relate to people in their social circle, and to deal with health professionals. CONCLUSION: The intervention was validated by various health professionals and pre-tested with four PLHIV. Preliminary results support the suitability and viability of the intervention. A randomized trial is currently underway to verify the effectiveness of the intervention in promoting optimal antiretroviral treatment taking.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".