Determinants of overall quality of life in people with advanced cancer
Bibliographic record
Abstract
People diagnosed with cancer experience a life-altering circumstance consisting in a multitude of battles. Due to longer survival rates, patients, clinicians, and researchers are concerned with the Quality of Life of people living with cancer. This is even more prevalent in the case of advanced cancer, when cure is less probable. The study on which this thesis is based provides a comprehensive conceptual, measurement, and methodological strategy to the study of Quality of Life and its health-related predictors. People recently diagnosed with a variety of advanced cancers had different aspects of their health evaluated such as biological factors, symptoms, functions, general health perceptions, and Quality of Life. We first illustrated the limitations of using a statistical approach that is commonly done, stepwise regression. It was demonstrated that using several measures of the same constructs complicates analyses and interpretation. A detailed qualitative evaluation of the content validity of the main measure of Quality of Life followed. Choosing only one measure of Quality of Life, we then compared the use of several more appropriate statistical modeling approaches for the use of multicorrelated data as is the case of Quality of Life. To evaluate the relationship between and among constructs, a Structural Equation Modeling approach was completed. We improved the currently available approach by the use of Rasch measurement models within the structural model approach. We finally explored the temporal trend of Quality of Life over time from the time of diagnosis and predicted group membership to these trends with data from the diagnosis. As the study of Quality of Life is a sophisticated science, optimal measurement and statistical methodological approaches must be employed. Otherwise, improper conclusions may be drawn that could lead to ineffective interventions. Using appropriate measurement and statistical approaches, we established that social support and function, fatigue, psychological well-being, gastro-intestinal symptoms, physical and psychological function were important contributors to the Quality of Life of people with advanced cancer. Cancer patients and survivors are commonly left to struggle with a host of physical and emotional issues that remain even when the cancer is cured. Yet few patients have access to the appropriate services for their disabilities, activity limitations, and restrictions resulting for the disease or from its treatments. This thesis provides foundational work for the creation of evidence-based programs that could answer the needs of people with cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".