Determinants of Job Rotation among Administrative staff of Tamale Polytechnic, Ghana
Bibliographic record
Abstract
This study employed descriptive survey to investigate the determinants of Job Rotation among the administrative staff at Tamale Polytechnic. A multi-stage sampling technique was employed for the study. The population was stratified into Senior Members (35), Senior Staff (45), and Junior Staff (55). Krejcie and Morgan (1970) table was used to select (32) Senior Members, (40) Senior Staff, and (48) Junior Staff respectively. Simple Random Sampling was used to select the required numbers from their respective populations. The instruments employed for the collection of data were questionnaires validated with the help of experts and pre-tested to ensure reliability. Regression analysis was used to analyse determinants of Job Rotation as assessed by Senior Members, Senior Staff, and Junior Staff computed at p < 0.05. The predictors in the construct were Age, Gender, Socio-cultural relationships, Performance, Training and Motivation of employees, while the ‘Number of Times Rotated’ in the institution formed the dependent variable. The assessment of Senior Members showed that, Performance, Training and Motivation of employees contributed largely to the explanation of the dependent variable ‘Number of Times Rotated’. This was found to be significant at .05, .04, and .03. Equally, Socio-cultural relationships, Performance, Training, and Motivation of employees contributed greatly to the explanation of the dependent variable ‘Number of Times Rotated’ and this was found to be significant at .03, .04, .04, and .03 as assessed by Senior Staff, and .01, .02, .04, and .02 as assessed by Junior Staff. To this end, the study recommends for the provision of a Job Rotation Policy that would clearly state the basis for such exercise to help increase the confidence of staff in the system.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".