Impact of supervisor support on work ability in an IT company
Bibliographic record
Abstract
BACKGROUND: Work ability is the ability of a worker to perform his job. The authors hypothesized that supervisor support influences the work ability of workers working in an information technology company located in Tokyo. AIMS: To assess whether or not support from supervisors influences work ability. METHODS: Two surveys using the Brief Job Scale Questionnaire and the Work Ability Index (WAI) were conducted, one in October 2007 and the other in October 2008 on the same cohort. Two cross-sectional analyses and a 1-year longitudinal analysis were conducted using multiple regression analysis. In addition, the relationships between supervisor support and each dimension of WAI were analysed separately. RESULTS: The number of participants was 1157 males. Significant relationships were observed between supervisor support and WAI scores in both survey periods after adjusting for age, job demand, job control, work group size, job rank and job type. The 2007 Supervisor support was a significant predictor of 2008 WAI that raised the possibility that supervisor support does influence WAI scores. From the analysis of each dimension of WAI, a strong relationship between supervisor support and WAI was observed for the sections of the WAI that assessed work capacity but not for the sections that assessed the personal health status of respondents. CONCLUSIONS: Supervisor support is an important predictor of work ability. Supervisor support is associated with the questions of the WAI that assess not only work demands but also person's resources of the work ability model.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".