Weight Changes and Body Image in Pregnant Women: A Challenge for Health Care Professionals
Bibliographic record
Abstract
Body changes concerns and body image dissatisfaction are common during pregnancy. We aimed to examine whether health care professionals (HCPs): (i) believe that women are concerned about body image during pregnancy; (ii) consider it important to question, support, and intervene when pregnant women express body image concerns; (iii) feel comfortable enough in their abilities to question pregnant women with concerns; and (iv) have sufficient knowledge and skills to provide adequate support. A 36-item e-survey, developed by ÉquiLibre in collaboration with an expert committee, was sent to HCPs via email. HCPs believe that some situations are associated with body image concerns: postpregnancy weight loss (74.0%), perceived changes in their appearance (65.9%), excessive weight gain (65.3%), and feeling less in control of their body (36.8%). Among 321 responders, 60% considered it important to question pregnant women’s concerns. One in four (25.4%) considered themselves “totally comfortable” asking about weight and body image concerns. Our study showed that HCPs need to be better supported in developing their abilities to help weight-preoccupied pregnant women. There is an urgent need to clarify HCPs’ roles and to delineate the referral process as well as to ensure staff availability, in terms of time and personnel.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".