Improving career wellbeing for first-time expectant mothers
Bibliographic record
Abstract
Within the diverse population of working women, those who experience pregnancy for the first time may face some particular challenges when it comes to their career development needs and issues. These include discrimination in the workplace, responding to social expectations and pressures, negotiating life roles, and evolving personal identities. This article discusses the major career problems encountered by this target group, both structurally and socially, with a focus on individual strategies to access personal agentic functioning and empower women facing these challenges. These workers are often overlooked in the career literature; yet, there is a range of career theories to draw upon to assist them in their needs. The application of the life-span, life-space career theory, and the narrative therapy approaches are explored in relation to the helping process. These two theoretical orientations were chosen as they address the particular challenges faced by pregnant women in the workplace, especially around negotiating life roles and an evolving personal identity. There is a need for a stronger understanding of these challenges and opportunities to support pregnant women as they seek vocational wellbeing, and how to tailor suitable, well-established career counselling strategies to meet their unique needs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".