Shame, Guilt, and Communication in Lung Cancer Patients and Their Partners
Bibliographic record
Abstract
Lung cancer patients report the highest distress levels of all cancer groups. In addition to poor prognosis, the self-blame and stigma associated with smoking might partially account for that distress and prevent patients from requesting help and communicating with their partners. The present study used innovative methods to investigate potential links of shame and guilt in lung cancer recovery with distress and marital adjustment. A specific emphasis was an examination of the impact of shame on partner communication. Lung cancer patients (n = 8) and their partners (n = 8) completed questionnaires and interviews that were videotaped. We report descriptive statistics and Spearman correlations between shame and guilt, relationship talk, marital satisfaction, distress, and smoking status. We coded the interviews for nonverbal expressions of shame. Greater self-reported shame was associated with decreased relationship-talk frequency and marital satisfaction, and with increased depression and smoking behaviour. Nonverbal shame behaviour also correlated with higher depression and increased smoking behaviour. Guilt results were more mixed. More recent smoking behaviour also correlated with higher depression. At a time when lung cancer patients often do not request help for distress, possibly because of shame, our preliminary study suggests that shame can also disrupt important partner relationships and might prevent patients from disclosing to physicians their need for psychosocial intervention and might increase their social isolation. Even if patients cannot verbally disclose their distress, nonverbal cues could potentially give clinicians an opportunity to intervene.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".