The Role of National Industrial Court in Sustaining Harmony in Nigerian Health Sector: A Case of University of Abuja Teaching Hospital
Bibliographic record
Abstract
Recently, Nigerian health sector especially the hospitals has been enervated by grievances, antagonism, unpleasantness, dissension, and apprehension. Unfortunately, the industry involved in ensuring workers’ healthcare and that of the populace has experienced tempestuous times. Slyly, issues whose pedigrees could be traced to superiority, autonomy, compensation schemes and other conditions of service gradually meandered into the public health sector leading to health workers and non-health workers being at loggerhead with one another. As such, the serenity and harmony once witnessed in government hospitals have been jumbled by incoherent differences of various groups in the hospital. This paper therefore proposes to examine the causes of disputes at the University of Abuja Teaching Hospital; what has been done, and what needs to be done by all and sundry and more especially, the role National Industrial Court (NIC) has played in sustaining harmony in Nigerian health sector. Also, it will examine the role National industrial Court has previously played and can still play futuristically to enhance and sustain the desired industrial harmony in University of Abuja Teaching Hospital, the entire health sector and other sectors of the economy.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.032 | 0.014 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".