Bibliographic record
Abstract
Health and safety is of the utmost importance for any company or institution to be successful. There is quite a negative perception regarding the health and safety of rural hospitals and clinics. Rural hospitals are most of the time overcrowded due the large amount of patients that has no medical aid, thus increases the risk for health and safety issues. Patients sit in long queues for hours to receive medical attention and their medication and are therefore exposed to all kinds of diseases, which is a high risk for these patients health. The employees working in these rural areas are also exposed to life-threatening diseases on a daily basis and have a good chance of being infected. Employees leave the public sector because of these unsafe working conditions and find themselves either working in the private sector or may even immigrate to foreign countries for better and safer working conditions. During this research done, there were a few shortcomings identified for the management to improvement on and to ensure a safe working environment. There are quite a lot of negativities surrounding the patients and employees in these rural hospitals, because patients get raped by nurses, babies get stolen from maternity wards, doctors are attacked by patients and much more horrific incidents happening in these hospitals. Cultural differences are also a main concern for management, because there are a lot of different races working together in the same department and not everyone has the same beliefs and ways in doing tasks. These cultural differences may lead to clashes amongst employees and result in a negative working environment. This quantitative research was done in selected rural hospitals, due to cost and time consumption. Only 80 employees (doctors, nurses and pharmacists) participated in the research done and the research was not an in-depth research, but enough evidence was compiled to make the necessary assumptions that all is not well in the public sector. With the new National Health Insurance (NHI) to be implemented from 2012, there may a lot of changes in the rural hospitals for the better. Hospitals all over the country are being upgraded and the working conditions are being attended to by the government which may attract more health professional to rural hospitals and clinics.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".