Sustaining hope: A narrative inquiry into the experiences of hope for nurses who work alongside people living with HIV in Ghana
Bibliographic record
Abstract
Aim: In this research, we explored how nurses working in HIV care in Ghana live and work with hope. Background: Nurses who work with people living with HIV have concerns about their well-being and quality of life. They also complain of stress-related workload due to high nurse-patient ratio. The study sought to examine the experiences of nurses in Ghana and the ways that hope is intertwined with their experiences in working with people living with HIV. Design: This study was a narrative inquiry study. Narrative inquiry is a collaborative way to inquire into participants' experiences in the three-dimensional spaces of temporality, sociality and place. Methods: We engaged with five nurses who work in an acute care setting where their primary focus is to provide care to people living with HIV. We engaged in six to eight conversations with each participant over several months. We asked participants to describe memories of significant experiences in their past and present lives, and share experiences that they would describe hopeful in their HIV nursing practice. Results: In this narrative inquiry study, four resonant threads emerged and included: (a) becoming a nurse for people living with HIV took time; (b) experiences of practising with hope were important; (c) faith in God, allowed them to gain strength, which was connected to hope; and (d) learning to live with hope was shaped by childhood experiences.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".