Incorporation of Nurse Initiated Management of Antiretroviral Treatment course within the undergraduate nursing programme North West province
Bibliographic record
Abstract
Background: According to the 90-90-90 strategy, the focus is on 90% of people living with HIV and/or AIDS knowing their HIV status, initiated on antiretroviral treatment and achieving viral suppression. The challenge is that only 74% of people living with HIV and/or AIDS are on antiretroviral treatment, and HIV mortality still occurs. Literature recommends the incorporation of a Nurse Initiated Management of Antiretroviral Treatment (NIMART) course within the undergraduate nursing programme to capacitate new nurses to manage people living with HIV and/or AIDS immediately after completion of their training. However, the NIMART course is still not incorporated, and there is dearth of information on this topic in North West Province (NWP). Aim: To explore and describe nurse educators' perceptions regarding the incorporation of NIMART course within the undergraduate nursing programme in NWP. Setting: The setting of this research study was nursing education institutions of the NWP. Methods: Phenomenography qualitative research design was followed. Twelve nurse educators underwent purposive selection and unstructured individual interviews were conducted. The research co-coder verified the findings. There were ethical considerations and trustworthiness maintained throughout the study. Results: Main themes that emerged in this study depicted benefits and challenges associated with NIMART course incorporation within the undergraduate nursing programme as stated in Table 1. Conclusion: This study concluded that NIMART course incorporation within the undergraduate nursing programme is a good and relevant idea, which requires human and non-human resources. Contribution: The study contributed new knowledge on how nurse educators perceive the NIMART course incorporation within the undergraduate nursing programme in NWP.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".