ERGONOMICALLY DESIGNED WORKSPACES, INSTITUTIONAL COMMITMENT AND SECRETARIES WELLBEING IN DELTA STATE TERTIARY INSTITUTIONS
Bibliographic record
Abstract
This research investigated Ergonomically designed workspaces, institutional Commitment and Secretaries well-being in Delta State tertiary institutions. Descriptive survey design was used to survey 110 professional secretaries across six universities, three colleges, and three polytechnics. A structured questionnaire with a 5-point Likert scale was used to collect data. The instrument was validated by three experts. Test-Retest was conducted and PPMC yielded r = 0.81, p < .05. Data analysis involved descriptive statistics and the Pearson Product Moment Correlation (PPMC) to examine relationships between the variables. The findings revealed that secretaries perceived their workspaces as inadequately designed ergonomically, with issues like uncomfortable chairs, poor lighting, and limited access to ergonomic accessories. Secretaries reported that poor ergonomic conditions negatively affect their health, leading to discomfort, fatigue, and stress. Conversely, they also reported that engaging in ergonomic practices like taking breaks and stretching helps reduce discomfort. The study found a strong positive correlation between perceived ergonomic workspace and management’s attention to ergonomic issues, meaning that the secretaries see ergonomic environment as supportive. A major recommendation from the study is that institutions should develop clear policies and allocate resources specifically for ergonomic improvements. Regular training sessions and routine assessments are also vital to enhancing secretaries’health and performance.
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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 | 0.002 | 0.003 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| 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".