Factors related to the career satisfaction of older managerial and professional women
Bibliographic record
Abstract
Purpose The labour force participation of older women has increased substantially in Canada. This study aims to examine the factors that are important to the career satisfaction of older managerial and professional women. Design/methodology/approach Managerial and professional women aged 50 and above completed a questionnaire assessing their career satisfaction, individual characteristics and organization‐related factors. Findings For managerial women, the significant predictors of career satisfaction were perceived as organizational support, job content plateauing, and health status. For professional women, the significant predictors of career satisfaction were perceived efforts by their organization to retain its older managerial and professional employees and job content plateauing. Research limitations/implications The findings are based on a small sample and the respondents were primarily employed in the public sector. Further research is needed using larger samples and a better representation from the private sector. Researchers also need to identify other factors that influence the career satisfaction of older managerial and professional women. Practical implications The career satisfaction of older managerial and professional women is heightened when they are challenged by their job and have an opportunity to learn and grow in their job. Beyond this, enhancing the career satisfaction of older managerial and professional women will require different approaches tailored specifically to each group. Originality/value Very little is known about the career‐related issues that are of special concern to older managerial and professional women. This study provides some insight into the differences between older managerial and professional women and the factors that contribute to their career 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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".