A GENERAL PERSPECTIVE ON THE IMPACT THAT RELIGIOUS AND SPIRITUAL BELIEFS HAVEWHILE ADAPTING TO NEOPLASTIC DISEASE.
Bibliographic record
Abstract
Apparently, religious orientation, religious and spiritual beliefs are regarded as important factors in adapting to the disease.Religious orientation as opposed to religious coping is seen as having a lower impact on one’s adapting to the disease from the perspective of life quality. It is supposed that both religious orientation and religious coping are equally efficient in increasing one’s life quality.The aim of the research is to highlight how the spiritual (inner peace, faith, meaning) and religious factors (religious coping and religious orientation), as predictors of increasing quality of life, contribute to increasing the quality of life of cancer patients.The sample is made up of cancer patients (161), mixed sample, women and men, between the ages of 30 and 70, admitted to the Radiotherapy of the Municipal Hospital of Timisoara. We are using the following instruments: Brief Measure of Religious Coping (RCOPE- Pargament K.I., SmithB.W. , Koenig H.G. and Perez L., 1998), Quality of Life in Adult Cancer (QLAC - Nancy E. A., Smith K.W., McGraw S., SmithR.G, PetronisV.M and Carver C.R.), Religious Orientation Scale Revised (ROS-R- Gorsuch R.L. and McPherson S.E., 1989), Functional Assessment of Chronic Illness Therapy - Spiritual Wellbeing Scale (Andrea L. Canada, Patricia E. Murphy, George Fitchett, Amy H. Peterman şi Leslie R. Schover, in 2007).Hierarchical multiple regression was used in data analysis.The results of the study indicate that the factors with the highest weight in explaining the majority of the quality of life are: personal extrinsic religious orientation, negative religious coping and inner peace.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".