Trend of health worker’s strike at a Tertiary Health Institution in North Central Nigeria
Bibliographic record
Abstract
Objectives: Workers’ strike is a global phenomenon since antiquity. In Nigeria, health-care sector has been rocked by series of strikes spanning variable periods with immeasurable losses. Ethical consideration and inter-professional rivalry are the main concern attracting much debate in the health sector. The objectives of the study were to determine the trend of health worker’s strike actions, the main agitators, and to make some recommendations. Material and Methods: This was a retrospective study of the labor ward records of the Jos University Teaching Hospital from January 1, 1985, to December 31, 2019, duration of 35 years. The data were collated and analyzed using simple percentages and the figures corrected to the nearest decimal point. Results: A total of 42 strike actions, about 2 strikes/year. The trend shows a multi-modal pattern, with the highest peak of 5 strikes in 2004 and 2013. There were cumulatively 58.5 months of strikes out of the 442 months of the period of study, giving a percentage of 13.2%. While doctors had more frequent strikes (52.3%), non-doctors under the umbrella of Joint Health Sector Union and nurse/midwives accounted for over half (58.1%) of the duration of the strikes. The resident doctors are the main agitators of doctors’ strike accounting for about half (45.2%) of the total health workers’ strikes, while NMA accounted for only 3 (9.4.%). Most strike actions occur at the end of the year, with spill into the first quarter of the following year. Conclusion: Health workers’ strike remains a perennial problem. Inter-professional rivalry is a major challenge in the health sector with far reaching implication without immediate government intervention. Addressing challenges in the residency training program will go a long way in reducing doctors’ unrest in the health sector.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".