Organizational practices supporting women and their satisfaction and well‐being
Bibliographic record
Abstract
Purpose The purpose of this exploratory study is to examine the relationship of the perceived presence of organizational practices designed to support women's career advancement and their work and extra‐work satisfaction and psychological well‐being. Design/methodology/approach Data were collected from 98 early career women in Australia using anonymously completed questionnaires. Five organizational practices combined into a composite measure were considered; top management support and intervention, policies and resources, use of gender in human resource management, training and development initiatives and recruiting and external relations efforts. Findings Women reporting more organizational practices supportive of women, with higher levels of job and career satisfaction, and indicated fewer psychosomatic symptoms and less emotional exhaustion. Organizational practices were unrelated to intent to quit or extra‐work satisfactions and physical or emotional well‐being. Research limitations/implications Further research is needed to determine if results generalize to women in later career stages. Practical implications Guidance for organizations interested in supporting women's career advancement are offered. Originality/value The paper illustrates an understanding of the qualities that are part of work environments that are supportive of the career aspirations of women (and men).
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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 | 0.002 |
| 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) | 0.001 | 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".