Nurses’ Experiences of Adverse Events Management at a Public Hospital, Gauteng Province
Bibliographic record
Abstract
BACKGROUND: Involvement in adverse events can be a traumatic experience that leaves the nurses with professional and personal distress. Some feel as though they have failed the patient doubting their nursing skills. While the effects of the event can be distinctively evident on the patient and hospital, the nurses in question often suffer in silence. The purpose of this study was to explore and describe the nurses’ experiences of adverse events management at a public hospital, and to develop recommendations to address them. METHODS: A qualitative, phenomenological and contextual research design was used to explore and describe the nurse’s experiences of management of adverse events at this public hospital in Gauteng, South Africa. A total of 18 professional nurses who met the inclusion criteria were purposively sampled. Data was collected by means of in-depth semi structured individual interviews and documented field notes. An audio tape recorder was used with the participants’ consent to capture the participant’s responses. Data was analyzed using Tesch’s open coding method. Ethical principles to protect the rights of the participants were adhered to, and the criteria of trustworthiness was ensured. RESULTS: Findings of the study revealed that participants experienced negative management of adverse events. Three sub- themes emerged namely: inconsistency in the reporting and recording of adverse events, lack of managerial support and unplanned job rotation used as punishment following the events. CONCLUSION: Involvement in adverse events has a negative impact on the nurses’ wellbeing as well as patient care. Management should make efforts to promote awareness, implement positive management of adverse events, ensure consistency in the reporting and recording of adverse events including the provision of managerial support, and planned job rotation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".