Psychological adaptation to cancer in youth: An investigation into the mechanisms of psychopathology and resilience
Bibliographic record
Abstract
Cancer diagnosis and treatment are a stressful experience for a patient of any age. However, compared to younger children or adults, adolescents and young adults (AYA) are a distinct group of patients with unique challenges due to their developmental period. Literature suggests that up to a third of AYA patients may be at risk for developing psychopathology. While there is an increasing interest in studying psychosocial outcomes in AYAs, little is understood about the mechanisms of psychosocial wellbeing in this population. This thesis uses a variety of research methods to examine factors associated with resilience and psychopathology in youth living with cancer and beyond cancer and aims to identify intervention targets to promote holistic recovery of young people with cancer. Because psychosocial oncology is a young and rapidly evolving field, I begin by a short overview of some of the methodological challenges I faced in my research. In particular, I wanted to address the issue of not using cancer-specific questionnaires, and lack of longitudinal and data-driven approaches. To this end, I have innovatively used a mixed-methods approach to explore how individual, as well as socio-environmental factors are associated with psychological outcomes in young patients and survivors. I begin by a systematic review of the literature, which provides an insight into the prevalence rates of psychopathology and identifies some of the risk factors for psychiatric disorders. Subsequently, I present qualitative analysis of in-depth interviews about life with and beyond cancer. For my third study, I developed an online survey to collect information about psychological wellbeing, including cancer-specific questionnaires. Using this data, I employed network analysis to explore if any symptoms of psychopathology and cancer worries are particularly salient and contribute more to the overall symptomatology than the rest. Lastly, I conclude with a longitudinal analysis of a clinical dataset in Canada, where I explored psychological wellbeing and its relationship with fatigue, a commonly endorsed symptom. Due to the global pandemic COVID-19 and its consequent disruption, I complemented my online survey with an auxiliary study that explored the impact of the pandemic on the wellbeing of young people with cancer who were considered at risk for infections. Together, the research presented in this thesis contributes evidence to support that a subset of young people with cancer report psychopathology, and those at risk may be particularly vulnerable in the times of distress, such as the global pandemic. However, psychopathology may result from vastly different, individually led modifiable pathways, which can be addressed with a more personalized approach to care in the future. Psychosocial adaptation to cancer in young people is a complex process with many factors at play. This thesis begins to uncover a few, introduces innovative methods to counteract the existing mythological pitfalls and concludes by proposing future directions for strengthening research in psychosocial oncology and reducing the inequalities of outcomes in young people with cancer.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".