Single, childless working women's construction of wellbeing: On balance, being dynamic and tensions between them
Bibliographic record
Abstract
OBJECTIVE: Single, childless working women (SCWW) are a notable proportion of the female workforce. The budding research on this population suggests that they have issues of wellbeing that may be tied to specific needs of both their workplaces and their personal lives, and hence, distinct work-life dynamics that require attention. This study explores how SCWW construct their wellbeing. PARTICIPANTS: The sample was composed of 22 SCWW aged 29 to 45. METHODS: A discourse analysis of the transcripts of semi-structured interviews with these women was performed. RESULTS: Most women drew on an interpretative repertoire of "wellbeing as balance" (e.g., diversification and reasonable dosing of life's dimensions). It was associated with a recurrent subject position we have termed "the dynamic woman" whose intensity transfused talk of the activities in her life. Here, work becomes a "passion" and a source of appreciated challenges. However, a dilemma could arise from these constructions for positioning oneself in relation to the cadence of one's active life or rather, in articulating an unambiguous claim to balance. Balance/dosing and dynamicity/passion can be uneasy bedfellows. CONCLUSIONS: Our analyses raise questions about possible counter[balancing] discourses and further argue the relevance of work-life issues for SCWW.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".