Determinants of Unethical Behavior by Stakeholders in the Medical Insurance Industry in Zimbabwe: An African Humanism (Hunhu/Ubuntu) Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 <em>Hunhu/Ubuntu </em>as a requirement for ethical practices that bring about good business and moral practices, it does not show how <em>Hunhu/Ubuntu </em>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 <em>Hunhu/Ubuntu</em> moulds good behaviour and is essential for avoiding risky behaviour which curtails organizational performance<strong>.</strong>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it