Factors Associated With Nurses’ Negative Behaviour at a Public Health Facility in Namibia
Bibliographic record
Abstract
Caring behaviour is central to all health care organizations and their employees. Nurses spend considerable time with patients and they are ethically bound to provide quality nursing care, regardless of their patients’ social class, gender or ethnic background. To improve patients’ experience of care, it is important that nurses are aware of factors – both within themselves and in their working environment – that might influence their attitude and behaviour. The purpose of this study was to describe and assess factors contributing to nurses’ negative behaviour at a public health facility in Namibia. A quantitative, non-experimental, explorative and descriptive design was used. Simple random sampling was used to select 64 nurses. A pre-tested questionnaire was used to collect data, which were then analysed with the Statistical Package for the Social Sciences (SPSS), version 23, using descriptive statistics. The study results showed a strong association of nurses’ negative behaviour to management’s reluctance to address the nurses’ negative behaviour, 85.9% (55) and patients’ behaviour and cultural practices 73.4% (47). The study recommended that rewarding nurses who display acceptable behaviour would be one of the cost effective strategy to motivate nurses who display professional behaviour.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".