Exploring the psychological and religious perspectives of cancer patients and their future financial planning: a Q-methodological approach
Bibliographic record
Abstract
BACKGROUND: Cancer patients are often hesitant to talk about their mental health, religious beliefs regarding the disease, and financial issues that drain them physically and psychologically. But there is a need to break this taboo to understand the perceptions and behaviours of the patients. Previous studies identified many psychological factors that are bothering cancer patients. However, it still requires exploring new elements affecting their mental and physical health and introducing new coping strategies to address patients' concerns. METHODS: The current study aims to identify cancer patients' perceived attitudes towards the severity of illness, understand their fears, tend towards religion to overcome the disease, and future financial planning by using a Q-methodological approach. Data were collected in three steps from January-June 2020, and 51 cancer patients participated in the final stage of Q-sorting. RESULTS: The findings of the study are based on the principal component factor analysis that highlighted three essential factors: (1) feelings, (2) religious beliefs about the acceptance of death, and (3) their future personal and financial planning. Further, the analysis shows that the patients differ in their beliefs, causes and support that they received as a coping mechanism. CONCLUSION: This study explains cancer patients' psychological discomfort and physical pain but cannot relate it to co-morbidities. Q methodology allows the contextualization of their thoughts and future planning in different sets, like acceptance of death, combating religion's help, and sharing experiences through various platforms. This study will help health professionals derive new coping strategies for treating patients and financial managers to design insurance policies that help them to share their financial burdens.
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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 | 0.044 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".