Competing priorities: a qualitative study of how women make and enact decisions about weight gain in pregnancy
Bibliographic record
Abstract
BACKGROUND: Despite ample clinical evidence that gaining excess weight in pregnancy results in negative health outcomes for women and infants, more than half of women in Western industrialized nations gain in excess of national guidelines. The influence of socio-demographic factors and weight gain is well-established but not causal; the influence of psychological factors may explain some of this variation. METHODS: This is the qualitative portion of an explanatory sequential mixed-methods study designed to identify predictive psychological factors of excess gestational weight gain (QUAN) and then explain the relevance of those factors (qual). For this portion of the study, we used a qualitative descriptive approach to elicit 39 pregnant women's perspectives of gestational weight gain, specifically inquiring about factors determined as relevant to excess gestational weight gain by our previous predictive study. Women were interviewed in the latter half of their third trimester. Data were analyzed using a combination of unconstrained deductive content analysis to describe the findings relevant to the predictive factors and a staged inductive content analytic approach to examine the data without a focus on the predictive factors. RESULTS: Very few participants consistently made deliberate choices relevant to weight gain; most behaviour relevant to weight gain happened with in-the-moment decisions. These in-the-moment decisions were influenced by priorities, hunger, a consideration of the consequence of the decision, and accommodation of pregnancy-related discomfort. They were informed by the foundational information a woman had available to her, including previous experience and interactions with health care providers. The foundational information women used to make these decisions was often incomplete. While women were aware of the guidelines related to gestational weight gain, they consistently mis-applied them due to incorrect understanding of their own BMI. Only one woman was aware that weight gain was linked to maternal and infant health outcomes. CONCLUSIONS: There is an important role for prenatal providers to provide the foundational information to positively influence in-the-moment decisions. Understanding how weight gain guidelines apply to one's own pre-pregnancy BMI and comprehending the well-established link between gestational weight gain and health outcomes may help women prioritize healthy weight gain amongst many competing factors.
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 | 0.023 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".