Young Women Who Are Doing Well with Changes Affecting Their Work: Helping and Hindering Factors
Bibliographic record
Abstract
This study responds to a call for an increased understanding of women workers and the importance of considering women’s experiences at different ages and stages of career involvement. Informed by positive psychology, this research looked at a small sub-set of working individuals, young women who selfidentified as doing well with changes affecting their work. The study focused on their experience of change, what strategies helped or hindered these young women in doing well, and what would have helped within the context of volatile and changing work conditions. The article describes the participants’ views regarding what change meant to them, along with the impact and result of changes they had experienced. Using the Enhanced Critical Incident Technique methodology (ECIT), the 10 participants reported a total of 147 helping and hindering, and wish list items. These break down into 85 helping incidents (58% of the total), 37 hindering incidents (25%), and 25 wish list items (17%) that were best represented by 9 categories: Friends and Family, Management and Work Environment, Skills Training and Self Growth, Personality Traits and Attitudes, Self-care, Personal Boundaries/Self Awareness, Take Action, School Pressure/Workload and Personal Change/Stressful Events. Implications for research, counselling practice and career counselling are discussed.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".