Professional nurses’ experience in providing nursing care to women experiencing gender-based violence: A caring presence study
Bibliographic record
Abstract
BACKGROUND: Women in a sub-district of one of the most rural provinces in South Africa are at a high risk of experiencing gender-based violence. Professional nurses are at the frontline of providing healthcare to these women. Caring presence is a valuable resource to professional nurses in these settings. AIM: To explore and describe the experiences of professional nurses in providing nursing care to women experiencing gender-based violence. SETTING: A primary health clinic, community health centre, out-patient department and emergency department in the rural sub-district. METHODS: = 15). RESULTS: Participants were willing to provide nursing care but worked in difficult environments, and their level of competence influenced how they engaged with these women. They realised that the lifeworld of the women made it difficult for them to disclose that they are experiencing gender-based violence. Participants emphasised the importance of multidisciplinary and multi-sectoral collaboration. A final theme, caring presence, also emerged. CONCLUSION: Participants felt compassion and were willing to provide nursing care. However, they experienced reluctance due to hindrances that limited them in connecting with and attuning to the women. This left them feeling frustrated, and with a deepened sense of empathy, as they realised how deeply the women are suffering. Recommendations were formulated. CONTRIBUTION: This study revealed nurses' need to be guided in providing relational care to women who are experiencing gender-based violence. Based on the findings, it is recommended that infrastructure should be updated to ensure private and safe spaces for women, debriefing and training should be provided and multidisciplinary collaboration should be strengthened. Policy for improved referral systems, the assessment and management of women experiencing gender-based violence and the wellness of professional nurses should be developed.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".