Organisational Pressure on Quality-of-worklife of Women in Tertiary Institutions in Lagos State, Nigeria
Bibliographic record
Abstract
An approach to motivation in the contemporary world of work is the implementation of Quality-of-Worklife (QWL) programmes, which is aimed at easing the pressures faced at work by employees. Quality-of-Worklife is a philosophy of improving productivity by providing workers with the opportunities required to put in their best at work, without jeopardizing their personal self improvement and responsibilities at home. The effectiveness of organisational factors and QWL programmes in the Nigerian tertiary institutions is under-researched. This study, therefore, investigates the organisational pressure on Quality-of-Worklife of women in tertiary institutions in Lagos State, Nigeria. It is a survey of the ex-post facto type. 3,640 senior cadre women working in the four purposively selected degree awarding institutions in Lagos State (University of Lagos, Lagos University Teaching Hospital, Lagos State University and Lagos State University Teaching Hospital) and their managers formed the population. Out of these, 1000 women and 19 managers were randomly selected as sample. A QWL survey and structured interview made up the instrument. Four hypotheses were tested at 0.05 significance level. There were significant differences among the institutions in the compliance to International Labour Organisation (ILO) conventions and in the Quality-of-Worklife of their women workers. Moreover, there were significant relationships between QWL and sources of pressure and QWL and organisational pressure. Factors such as salary, health-care benefit, day-care services, recognition, workload and others, influenced QWL of women. Government and management committees of the tertiary institutions should ensure the reduction of organisational pressure and promote high QWL of women workers, in other to improve on their productivity and promote organisational growth.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".