Barriers to tuberculosis and human immunodeficiency virus treatment guidelines adherence among nurses initiating and managing anti-retroviral therapy in KwaZulu-Natal and North West provinces
Bibliographic record
Abstract
BACKGROUND: Nurses, as front-line care providers in the South Africa's health care system, are called upon to deliver integrated interventions for tuberculosis and human immunodeficiency virus (TB and HIV) including nurse-initiated management of anti-retroviral therapy (NIMART) and anti-TB treatment. Adherence to treatment guidelines and factors associated with non-adherence to treatment guidelines among nurses remain under explored. PURPOSE: To explore and describe barriers to treatment guidelines adherence among nurses initiating and managing anti-retroviral therapy and anti-TB treatment in KwaZulu-Natal and North West provinces. DESIGN: This study employed a qualitative exploratory descriptive design. METHODS: Four semi-structured focus group interviews were conducted during 2014 each consisting of four to eight NIMART trained nurses. Audiotaped interviews were transcribed verbatim and analysed using Atlas T.I. software. FINDINGS: During data analysis, two themes emerged: (1) NIMART trained nurses' distress about TB and HIV guidelines adherence that is inclusive of lack of agreement with guidelines, poor motivation to implement guidelines, poor clinical support and supervision, resistance to change, insufficient knowledge or lack of awareness and (2) exterior factors inhibiting nurses' adherence to treatment guidelines which incorporated organisational factors, guidelines-related factors and patient-related factors. CONCLUSION: This qualitative study identified that nurses have substantial concerns over guideline adherence. If NIMART trained nurses' barriers inhibiting adherence to treatment guidelines cannot be remedied, patient outcomes may suffer and South Africa will struggle to meet the 90-90-90 targets.
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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 | 0.002 | 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 | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".