Can training and apprentice programs in STEM increase worker life satisfaction and optimism?
Bibliographic record
Abstract
Abstract Background Despite the significant relationship between life satisfaction and education, less is known about the connection between life satisfaction and informal learning in the context of training and apprenticeship programs. This paper examines the influence of the LaunchCode program, a novel training and apprentice program in STEM, on participant’s life satisfaction and optimism. We also explore mediating roles of STEM employment and earnings, as well as moderating role of participants’ educational attainment levels. Results We find high life satisfaction and optimism among those who completed both the training course and the apprenticeship component. In addition, we find a significant mediation effect of STEM employment on the relationships between program participation and current life satisfaction, as well as optimism, among the apprenticeship completers. Finally, we find a significant moderation effect of one’s education level on the relationship between program completion and finding a STEM job, such that participants with a college degree are more likely to secure STEM employment through coursework alone. Conclusions Our findings highlight the significance of apprenticeships in increasing life satisfaction and optimism, as well as the importance of STEM employment in explaining the significant effect of apprenticeships on life satisfaction and optimism. These findings suggest that what people do for a living is more important than how much they earn. However, while apprenticeships may offer an alternative route to the labor market, education may still facilitate connections to STEM employment in the absence of an apprenticeship.
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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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".