The Anxiety Depression Pathway Among Men Following a Prostate Cancer Diagnosis: Cross-Sectional Interactions Between Anger Responses and Loneliness
Bibliographic record
Abstract
Anger has been a largely neglected emotion in prostate cancer research and intervention. This paper highlights the role of anger in the anxiety depression pathway among men with prostate cancer, and whether its impact is dependent on loneliness. Data are presented from a sample of men with prostate cancer ( N = 105, M = 69.12 years, prostatectomy = 63.8%) and analysed using conditional process analysis. Dimensions of anger were evaluated as parallel mediators in bi-directional anxiety and depression pathways. Loneliness was evaluated as a conditional moderator of identified significant mediation relationships. Moderate severity depression (16.5%) was endorsed more frequently than moderate severity anxiety (8.6%, p = .008), with 19.1% of the sample reporting past two-week suicide ideation. Consistent with hypotheses, anger-related social interference (but not other dimensions of anger) significantly mediated the anxiety-depression pathway, but not the reverse depression-anxiety pathway. This indirect effect was conditional on men experiencing loneliness. Sensitivity analyses indicated the observed moderated mediation effect occurred for affective, but not somatic symptoms of depression. Findings support anger-related social interference (as opposed to anger frequency, intensity, duration or antagonism) as key to explaining the previously established anxiety-depression pathway. Results underscore the need for enhanced psychosocial supports for men with prostate cancer, with a particular focus on relational aspects. Supporting men with prostate cancer to adaptively process and manage their anger in ways that ameliorate negative social consequences will likely enhance their perceived social support quality, which may in turn better facilitate post-diagnosis recovery and emotional adjustment.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".