Introducing a Holistic Health Care Education Model that Employs the Difference Between Being Healed and Cured
Bibliographic record
Abstract
Abstract: The two authors of this article have been researching and discussing for many years the importance of a possible health care education curriculum that seriously considers the difference between being healed and being cured and the role spiritualty may play. While participating in a longstanding study of spirituality within healthcare, both authors observed their own experiences of having been diagnosed with “cancer” but still feeling healthy and happy. Despite their biological health circumstances, they have been living life to the fullest with optimism that they are able to function beyond any suppositions associated with a medical diagnosis. Thus, they have been revisiting their earlier research findings and their hypothesizing that there is a difference between being healed and being cured, which should be introduced into health care education. The authors conclude that there is considerable support in the medical research community for further work around the relationship between spirituality and being healed. Their objective has been to discover something their research participants have claimed, related to being healed or cured. And the authors wished to gain some understanding of their personal approaches to healing that might prove useful to mainstream medical practice in North America. In this research paper, the authors will report on what their long-standing research group, consisting of a medical professor, a religious studies professor, a professor of higher education, a sociology professor, a registered nurse and a Reiki healer have reported on so far.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".