Cancer Survivors’ Beliefs About the Causes of Their Insomnia: Associations of Causal Attributions With Survivor Characteristics
Bibliographic record
Abstract
Objectives: Insomnia is common among cancer survivors, yet survivors’ beliefs about their insomnia following cancer are largely unknown. This study describes cancer survivors’ causal attributions of insomnia and whether these beliefs differ by sociodemographic characteristics. Participants: 160 cancer survivors meeting diagnostic criteria for insomnia disorder. Methods: Survivors endorsed how likely they believed 12 different factors were causally related to their insomnia and self-reported sociodemographics. Multinomial logistic regression tested associations between attribution endorsement and sociodemographics. Latent class analysis (LCA) examined patterns of attribution endorsement and whether sociodemographics were associated. Results: One hundred fifty-four survivors (96%) endorsed that at least 1 causal attribution was likely related to their insomnia. Most survivors endorsed that emotions (77%), thinking patterns (76%), sleep-related emotions (65%), and sleep-related thoughts (57%) were related to their insomnia, similar to data previously published among healthy persons with insomnia. Younger participants were more likely to endorse that biochemical factors related to their insomnia (ps < .02); females were more likely to endorse that hormonal factors related to their insomnia (ps < .001). LCA identified three classes (AIC = 3209.50, BIC = 3485.13). Approximately 40% of survivors endorsed most of the causal attributions were likely related to their insomnia; 13% frequently endorsed attributions were neither likely nor unlikely to be related. Older survivors were more likely to belong to the 47% who reported most attributions were unlikely related to their insomnia (p = .03). Conclusions: Cancer survivors with insomnia commonly endorsed that thoughts and emotions contributed to their sleep disturbance. Survivors’ sociodemographic characteristics did not meaningfully explain individual differences for most causal attribution beliefs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".