Psychological adaptation to cancer in youth: An investigation into the mechanisms of psychopathology and resilience
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».