Unfair labour practice on staff in primary health care facilities, North West province, South Africa: A qualitative study
Bibliographic record
Abstract
BACKGROUND: Unfair labour practices on staff is a worldwide concern which creates conflicts and disharmony among health workers in the workplace. It is found that, nursing staff members are unfairly treated without valid reasons in primary health care (PHC) facilities and predominantly in the developing countries and South Africa is not an exception. OBJECTIVES: The purpose of the study was to explore and describe the experiences of operational managers regarding unfair labour practices on staff by their local health area managers, and describe the perceptions of operational managers towards such treatment. METHOD: A qualitative, descriptive, exploratory and contextual research approach was considered appropriate for the study. The population of the study comprised operational managers working in PHC facilities in the North West province, South Africa. Purposive sampling was used to select participants for the study and focus group interviews used to interview 23 operational managers. Ethical measures were applied throughout the study. RESULTS: The six phases of thematic analysis were used to analyse the data collected for the study. Two themes that emerged are experiences of factors related to unfair labour practices in the PHC facilities and the perceptions regarding how to improve their working conditions. The categories that were found in the first themes were favouritism and discrimination. In the second theme, in-service training and transparency regarding staff training and development emerged. Recommendations comprised, among others, training on the concepts of equality in the workplace, and reinforcement of transparency regarding granting of study leave and attending workshops. CONCLUSION: Operational managers in the PHC facilities experienced unfair labour practices as evidenced by favouritism and discrimination.
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.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 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".