Understanding mental health in breast cancer from screening to Survivorship: an integrative phasic Model and tool
Bibliographic record
Abstract
Integrative models of mental illness and health in psycho-oncology are aimed at all types of cancer, although the patients’ experiences and issues may vary. This review summarizes the different theories and models of mental illness and health pertaining to the breast cancer experience and proposes an integrative phasic model applicable to the breast cancer trajectory. Five databases were searched for studies related to breast cancer mental health and illness theories and models. The PRISMA checklist form was used to extract the essential information from the included studies. Eleven theories and models on the experience of breast cancer were found. The integrative model based on these theories and models illustrates that the breast cancer experience is conceptualized as a trajectory with seven landmark ‘events’, each associated with a pathogenic ‘challenge’ leading to six possible ‘symptoms’, 1) psychological distress with anxious features, 2) psychological distress with depressive features, 3) non-specific distress 4) psychological distress with trauma-related features 5) low health-related quality of life, and 6) fear of recurrence. The Breast Cancer Psychological Integrative Phasic Model is supported by a simple clinical tool (BreastCancerPsych – Integrative Clinical Tool) that serves as a valuable resource throughout the care trajectory. These integrative phasic model and clinical tool are designed to help mental health clinicians formulate treatments that are tailored to the needs of their patients, especially for trajectories that are not marked by resilience.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".