Are the Exiting Quality of Life Measures Appropriate for Muslim Patients with Cancer?
Bibliographic record
Abstract
Purpose: This article aims to review the appropriateness of five general quality of life (QoL) measures for the Muslim patients with cancer.Method: The literatures related to QoL in patients with cancer, published between 1981 and 2011 were critically reviewed. Several database databases including CINAHL, MEDLINE as well as PUBMED, ProQuest, Elsevier, Google scholar and reference list were included. There were 25 articles best fit the inclusion criteria. Books and journal articles addressing Islamic principles were also reviewed.Result: QoL is a complex, multidimensional, and subjective phenomenon. It has been defined differently but overlapping by many scholars in the field. The patient’s QoL is important since it is one of the indicators of quality cancer care. The EORTC QLQ C30, FLIC, McGill QoL are the examples of widely used QoL measures which are appropriate to be applied in Muslim cancer population, while the FACT-G and CARES SF need to be revised in some of their items. Issues related to Islamic principles are discussed to support needs of further revision of these QoL measures.Conclusion: Most of the QoL measures’ items are not conflicting with the Islamic principles, except some items. Psychometric properties of the revised measures appropriate for Muslim cancer population should be further examined so that applying these measures can provide valid findings. Furthermore future cross cultural study may be possible.
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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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".