Where Is Communication Breaking Down? Narrative Tensions in Obesity-in-Pregnancy Clinical Encounters [ID: 1377560]
Bibliographic record
Abstract
INTRODUCTION: Health care professionals (HCPs) often view obesity in pregnancy as a condition associated with numerous adverse clinical outcomes and procedural difficulties. In contrast, patients characterize their challenges encountered because of stigma, their desire for a normal pregnancy, and may feel weight is overemphasized. The study's objective was to explore stories of patients and HCPs in tandem, in order to understand communication challenges within obesity-in-pregnancy clinical encounters. METHODS: Employing narrative inquiry, we conducted in-depth interviews with 16 patients and 19 HCPs. Stigma theory informed our research approach. Dialogical narrative analysis of interview transcripts was conducted. RESULTS: We identified five narrative tensions that contributed to communication challenges during clinical encounters. The first three tensions related to contrasting views on obesity: 1) obesity as a detriment to health versus acceptance of obesity; 2) a result of personal choice versus a result of uncontrollable circumstances; and 3) a regular pregnancy versus a high-risk diagnosis. Two further tensions related to characterizations of communication within the clinical encounter: 4) typical and problem-free versus a tremendously difficult clinical encounter; and 5) talking openly about obesity versus sidestepping the topic. How participants positioned themselves relative to prevailing societal discourses regarding obesity and being a “good” HCP/patient influenced these tensions. CONCLUSION: This study identified five narrative tensions and revealed specific areas where communication in the obesity-in-pregnancy clinical encounter is vulnerable to breaking down, accounting for the complexities in this interactive space. These findings can inform clinical practice and education and may be applicable to other clinical contexts.
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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 | 0.015 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".