Framing Concerns about Body Image during Pre- and Post-Surgical Consultations for Head and Neck Cancer: A Qualitative Study of Patient–Physician Interactions
Bibliographic record
Abstract
Patients with head and neck cancer report high unmet psychosocial needs as they undergo lifesaving treatments that can significantly alter their appearance and cause functional impairments. This qualitative analysis of recordings of 88 pre- and post-surgical consultations involving 20 patients respond to the need for empirical studies of patient-provider conversations about body image concerns. It indicates that the emphasis on concerns about survival, cure, and physical recovery during clinical consultations may leave concerns about the impacts of surgery on appearance and function unexplored and even silenced. The interviews with patients and medical team members that complement the analysis of the recordings suggest that an emphasis on survival, cure, and physical recovery can respond to the need for reassurance in the context of serious illness. However, it can also be problematic as it contributes to the silencing of patients' concerns and to a potential lack of preparedness for the consequences of surgery. The results of this study can contribute to raising surgeons' awareness of the interactional dynamics during clinical consultations. Moreover, the results highlight the unique role that surgeons can play in validating patients' psychosocial concerns to support patients' rehabilitation in both physical and psychosocial domains.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".