The Spirituality Integration in Iranian Health Education: Best Lessons for Development
Bibliographic record
Abstract
Background and Aim : In recent decade, nursing and medical schools increasingly tend to incorporate spirituality education in curriculums. This study was conducted to determine the spiritual education status and the gaps in Iranian nursing and medical schools and identify the best interventional tools to improve. Materials and Methods : It was performed a descriptive-comparative research by using an advanced search in international and national databases to collect all related materials on spiritual education. Ethical Considerations : Moral rules such as honesty and integrity in the search, analysis, reporting of the searched literature and citation of the sources were taken into consideration. Findings : The final sample comprised 48 articles concerning the spirituality integration in nursing and medical education. According to didactic various aspects, the findings subdivided into the associated subjects including; spiritual care education status in nursing and medical schools, educational patterns, interventions evaluation and Iranian studies assessment. Conclusion : There is a leading gap regarding the spirituality integration in nursing and medical curriculums in Iran, while the religious context and the centralized management of health education in Iran can be considered as appropriate opportunities for this aim. Based on the findings, the development of a structured educational system, sensitizing educational stakeholders and their stimulation by competitive award programs can be considered to a more effective integration of spiritual training in the health curricula. Citation: Mehrabi M. Memaryan N. Mohaghegh N. Abbasi M. The Spirituality Integration in Iranian Health Education: Best Lessons for Development. Bioeth Health Law J. 2017; 1(2):55-61.
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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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".