Professional women's mid‐career satisfaction: an empirical exploration of female engineers
Bibliographic record
Abstract
Purpose The dynamics of professional women's mid‐career satisfaction are important to understand, given the vast knowledge, experience and skills typically accrued by mid‐career that are often difficult to replace. Design/methodology/approach This study empirically examines Auster's multilevel framework of factors affecting the mid‐career satisfaction of professional women using a sample of 125 professional women engineers. Findings Results of logistic regressions reveal that individual, career, job, stress and organizational factors all impact the mid‐career satisfaction of professional women, but that stress and job factors are the most powerful determinants for this sample of women. Research limitations/implications While this study offers many insights and possible directions for future research on women at mid‐career, there are a number of limitations. Future research could broaden the macro and micro factors explored, as well as compare these results with those of women in other fields and industries, women at other career stages, and women across other geographic regions. Practical implications Organizations should strive to be more transparent about advancement options and opportunities, provide interesting and challenging work and more flexibility in work schedules (emphasize output, not face time), and offer support for key drivers of stress. Originality/value This is the first fairly large‐scale empirical study of macro and micro factors affecting women's mid‐career satisfaction. This article should be of interest to managers concerned with retention of high‐performing employees, HR practitioners, and academics specializing in careers, women's issues, and human resource management.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".