Experiences of Low Gestational Weight Gain: A Phenomenological Study with Pregnant Women
Bibliographic record
Abstract
Low maternal, gestational weight gain is associated with preterm birth, intrauterine growth restriction, low birthweight, small-for-gestational-age infants, neural tube defects, infant death, failure to initiate breastfeeding, and childhood asthma. The advantage of qualitative research is it can provide valuable insights for health care professionals into the experience and perceptions of low gestational weight gain from the vantage point of women with first-hand lived experience. In this Heideggarian interpretive phenomenological study, the meaning and experiences of weight gain for pregnant women with low gestational weight gain were explored. Data were collected through interviews with 10 pregnant women from Atlantic Canada. Conroy’s pathway for interpretive phenomenology was utilized. A hermeneutical spiral of interpretation identified six patterns or major themes: confronting one’s mortality; defending oneself against a permanent metamorphosis into a stranger; playing with fire and brimstone; slipping under the radar; trying to find peace; and riding an emotional roller coaster. The findings point to a war that is being waged over pregnant bodies with respect to weight that leaves pregnant women fending for themselves, apparently with little help from their health care providers. Implications of the findings for health practice, education, and research 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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".