Bibliographic record
Abstract
Up to 80% of schizophrenic patients use religion to cope with their illness. These positive spiritual coping strategies are the primary predictor of mental wellness in patients with schizophrenia. Yet, most medical professionals have no religious training and are often ill-equipped to guide their schizophrenic patients in spiritual matters. Typically, religious institutions and modern medicine are not associated together, but what happens when mental health professionals lack the training to assist 80% of their schizophrenic patients who use religion as a coping strategy? Schizophrenic patients whose beliefs are not respected have a higher rate of suicide, face increased stigma and report a lower overall quality of life. Such patients are more likely to decline mentally and drop out of treatment. Some scholars, psychologists and philosophers are now arguing that ignoring the connection between religion and mental wellness is unethical because practitioners are failing to take patient diversity into account. This paper will attempt to answer the following questions: In an increasingly diverse world, is it the responsibility of mental health professionals to learn about their patients’ religious beliefs, especially when their beliefs are so closely intertwined with their chances at successfully managing their illness, such as the case with schizophrenics? Why is it a good idea to consider combining religion and healthcare? Should the increase in diverse patients require additional training for mental health professionals? Is it unethical for a mental health care professional to be ignorant of diverse cultures and religions? What are the dangers of allowing medical professionals, who largely have no training in religious affairs, to guide mentally ill patients? What are the potential solutions for this problem? Which solutions are more effective and why? Are the current practiced healthcare models, which combine medicine and religion, effective? Keywords: schizophrenia, religion, treatment, therapy, psychosis, stigma
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".