Self-Efficacy, Performance, Training and Well-Being of Industrial Workers in Lagos, Nigeria
Bibliographic record
Abstract
The main purpose of this study was to investigate whether Self- Efficacy (SE) has anything to do with industrialemployees’ training, performance and well-being in Nigeria industrial settings. Self-Efficacy (belief about one’sability to accomplish specific tasks) form a central role in the regulatory process through which an individual’smotivation and performance are governed. It also affects employees’ training and well-being. The descriptivesurvey research design of the ex-post facto type was adopted. The population for the study consisted ofemployees of SKG Lagos, Glaxo, Ikeja and Smithkline Beecham, Ogba. The simple random sampling techniquewas used to select 274 respondents for the study. Four research instruments structured on a modified four pointrating format of Strongly Agree (SA)=4, Agree (A)=3, Disagree (D)=2. Strongly Disagree (SD)=1 were used andhaving reliability coefficient of: Self-Efficacy Scale (SES)=0.85; Training Acquisition Scale (TAS)=0.80; WorkPerformance Scale (WPS)=0.82 and Well-being Scale (WBS)=0.87. Data were analyzed with t-test statistic. Thefinding revealed that workers with high self-efficacy are higher performers of assigned duties than those withlow self – efficacy, workers with high level of self-efficacy are more amenable to training than those with lowlevel of self – efficacy and workers with high self-efficacy are better in their well-being than those with low self– efficacy. It was recommended that industrial social worker should work on the psychic of the workers so thattheir self-efficacy can be developed or strengthen positively with the intent of promoting higher performance,adaptability to training and fostering of employees well-being.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".