“You kind of just have to give up on that dream”: Saskatchewan women’s and men’s experiences with fertility gaps
Bibliographic record
Abstract
Longitudinal research has shown that women and men are more likely to underachieve, than overachieve, their child number preferences (Quesnel-Valleé & Morgan, 2003). Such discrepancies between preferences and reality are known as fertility gaps (Chesnais, 2000; Phillipov, 2009). This thesis sought to further the qualitative work of Carvalho and colleagues (2018), who demonstrated that individuals in Brazil were often satisfied with their experienced fertility gaps. Using the Model of Dyadic Pathways (Brehm & Schneider, 2019), a context-based approach was taken to explore the unique ways in which women and men experience timing and number gaps. Biographical narrative interviews were conducted in-person or via Zoom with women and men who mostly resided in Saskatchewan, Canada. After applying purposive sampling techniques, the final sample consisted of seven women and seven men who had or wanted (more) children, ranging in age from 27 – 41 years. Interpretative Phenomenological Analysis was used to explore participants’ perceptions of experienced or anticipated fertility gaps. Participants associated multiple factors with fertility gaps (e.g., infertility, completing education, establishing job stability, and the COVID-19 pandemic) and often made sense of timing and number gaps by explaining their perceived level of control over circumstances that affected their child-related decisions. For example, women and men often expressed an openness to situations that they felt were beyond their control (e.g., romantic relationship formation) and came to accept timing and number gaps despite alternative preferences. Participants also talked about choosing fertility gaps, whether out of necessity or due to strong desires to satisfy other life aspirations first. Some women and men expressed negative feelings towards fertility gaps, such as worry and stress. Though many participants accepted the discrepancies they talked about, they noted a number of supports that would (or did) assist them in their decision-making (e.g., financial, social, and informational support). The results suggest that maintaining and improving policies and practices (e.g., related to childcare, financial incentives, and parental leave) can help people navigate their fluctuating preferences and make decisions about children easier. Having available, accessible, and affordable supports may encourage agency in peoples’ decisions about children, in a way that they can choose whether or not fertility gaps are suitable to them. A provincial needs assessment would be useful to identify how to best support people in Saskatchewan.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.022 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".