Where is communication breaking down? Narrative tensions in obesity-in-pregnancy clinical encounters
Bibliographic record
Abstract
There are numerous biomedical and psychosocial challenges associated with obesity in pregnancy that impede communication between healthcare providers (HCPs) and patients. We conducted a narrative study informed by stigma theory to understand specific areas of communication breakdown in obesity-in-pregnancy clinical encounters. Sixteen patients and 19 HCPs participated in in-depth, semi-structured interviews. We explored how participants positioned obesity-in-pregnancy clinical encounters within their broader narratives. Employing narrative analysis, we identified five narrative tensions contributing to communication challenges: 1) obesity as a detriment to health versus an acceptable biologic variation; 2) obesity as the result of personal choice versus the result of uncontrollable circumstances; 3) a regular pregnancy versus a high-risk diagnosis; 4) a typical and problem-free clinical encounter versus a tremendously difficult clinical encounter; and 5) talking openly about Body Mass Index (BMI) and related co-morbidities versus sidestepping the topic. How participants positioned themselves relative to prevailing societal discourses regarding obesity in general influenced these tensions. These narrative tensions revealed specific areas where communication is vulnerable to breaking down during the obesity-in-pregnancy clinical encounter. Participants' (both HCPs and patients) past experiences of clinical encounters-and the meanings they ascribe to them-shape subsequent encounters, and our analysis illuminates the complexities of this interactive space. This research has implications for improving clinical practice and education.
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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 | 0.025 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.017 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 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".