doi:10.1155/2011/835945 Review Article Complementary Spiritist Therapy: Systematic Review of Scientific Evidence
Bibliographic record
Abstract
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Spiritism is the third most common religion in Brazil, and its therapies have been used by millions worldwide. These therapies are based on therapeutic resources including prayer, laying on of hands, fluidotherapy (magnetized water), charity/volunteering, spirit education/moral values, and disobsession (spirit release therapy). This paper presents a systematic review of the current literature on the relationship among health outcomes and 6 predictors: prayer, laying on of hands, magnetized/fluidic water, charity/volunteering, spirit education (virtuous life and positive affect), and spirit release therapy. All articles were analyzed according to inclusion/exclusion criteria, Newcastle-Ottawa and Jadad score. At present, there is moderate to strong evidence that volunteering and positive affect are linked to better health outcomes. Furthermore, laying on of hands, virtuous life, and praying for oneself also seem to be associated to positive findings. Nevertheless, there is a lack of studies on magnetized water and spirit release therapy. In summary, science is indirectly demonstrating that some of these therapies can be associated to better health outcomes and that other therapies have been overlooked or poorly investigated. Further studies in this field could contribute to
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.062 | 0.008 |
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".