Experiences of nurses returning to work after the second/third childbirth and organizational support in Southeastern China: A qualitative exploration
Bibliographic record
Abstract
Background: Since China encounters challenges of the aging population, especially a decrease in the working-age population, the Chinese government officially ended the one-child policy and has implemented the second/third child policies. This study aimed to explore nurses’ experiences, the supports they received from hospitals, and the supports they expected when returning to work after the second/third childbirth.Methods: Asynchronous focus groups and one-on-one in-person interviews were held with nurses from three hospitals in Wenzhou city who had returned to work within three months following their second/third childbirth. Data collection and analysis were conducted using Maslow’s Hierarchy of Needs as a guiding framework.Results: Twenty-three nurses were included in this study. Themes emerged around five needs, including physiological, safety, love and belonging, esteem, and self-actualization. Regarding these needs, nurses expressed receiving some support, including support from managers and colleagues and flexible scheduling assignments. However, there was also a lack of other supports, including dedicated time and space for breastfeeding, parental leave, nearby and affordable childcare, unit assignment and working schedules considering individual circumstances, mental health support, and opportunities for professional development.Conclusions: Our findings highlight the necessity to dedicate resources to support the diverse needs of returning nurse mothers such that they can balance family life and work. While longer-duration studies with larger sample sizes in other regions of China are needed, our findings also suggest future studies on exploring third childbirth experiences, evaluating supportive interventions, and creating a specific theoretical model on the comprehensive needs of nurses returning to work after childbirth.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".