Determinants of Unethical Behavior by Stakeholders in the Medical Insurance Industry in Zimbabwe: An African Humanism (Hunhu/Ubuntu) Approach
Bibliographic record
Abstract
There is a continuous decline in the performance of medical insurance companies in Zimbabwe resulting in these companies failing to meet their obligations to stakeholders as seen by failure to pay wages, policy holders’ medical bills and dividends to shareholders. While research shows Hunhu/Ubuntu as a requirement for ethical practices that bring about good business and moral practices, it does not show how Hunhu/Ubuntu influences stakeholders, employee behaviour and organizational performance. Due to this glaring gap, the study was designed to investigate: the causes of unethical behaviour in the medical insurance industry, the attributes of African Humanism and how it influences people’s behaviour in medical insurance firms. A case study research design was used where both quantitative and qualitative methodologies were employed. Closed and open-ended questionnaires, semi-structured interviews and focus group discussions were conducted. Chi-square tests were used for data analysis. Findings of the study show that Hunhu/Ubuntu moulds good behaviour and is essential for avoiding risky behaviour which curtails organizational performance.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".