Predictors of Employer Satisfaction: Technical and Non-technical Skills
Bibliographic record
Abstract
Employers of 2007-2009 graduates from Iowa State University College of Veterinary Medicine were asked to respond to a survey regarding their overall satisfaction with their new employees as well as their new employees' preparation in several technical and non-technical skill areas. Seventy-five responses contained complete data and were used in the analysis. Four technical skill areas (data collection, data interpretation, planning, and taking action) and five non-technical skill areas (interpersonal skills, ability to deal with legal issues, business skills, making referrals, and problem solving) were identified. All of the skill area subscales listed above had appropriate reliability (Cronbach's alpha>0.70) and were positively and significantly correlated with overall employer satisfaction. Results of two simultaneous regression analyses indicated that of the four technical skill areas, taking action is the most salient predictor of employer satisfaction. Of the five non-technical skill areas, interpersonal skills, business skills, making referrals, and problem solving were the most important skills in predicting employer satisfaction. Hierarchical regression analysis revealed that all technical skills explained 25% of the variation in employer satisfaction; non-technical skills explained an additional 42% of the variation in employer satisfaction.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.005 | 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".