Resilience in the Face of Cancer: On the Importance of Defining and Studying Resilience as a Dynamic Process of Adaptation
Bibliographic record
Abstract
Resilience is defined as the maintenance or relatively quick recovery of mental health during and after adversity. Rather than focusing on psychopathology and its causes, resilience research aims to understand what protective mechanisms shield individuals against developing such disorders and translate these insights to improve psychosocial care. This resilience approach seems especially promising for the field of oncology because patients face stressor after stressor from diagnosis to survivorship. Helping patients to learn how they can best use the resources and abilities available to them can empower patients to handle subsequent stressors. In the past few decades, resilience has increasingly been considered as a dynamic process of adaptation. While researchers use this definition, resilience has not yet been studied as a dynamic process in the field of oncology. As a result, the potential of resilience research to gain insight into what helps protect cancer patients from developing psychopathology is limited. We discuss conceptual and methodological proposals to advance resilience research in oncology. Most importantly, we propose applying prospective longitudinal designs to capture the dynamic resilience process. By gaining insight in how cancer patients engage in protective factors, resilience research can come to its full potential and help prevent psychopathology.
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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 | 0.018 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.009 |
| 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".