The Search for Truth: The Case for Evidence Based Chaplaincy
Bibliographic record
Abstract
Chaplaincy and medical science are in search of truth. Should chaplaincy become more scientific in response to health care reform? Yes is the answer. Chaplaincy ought to become more based in evidence for the following reasons. First, the health care culture is evidence based and chaplaincy needs to speak that language. Second, chaplaincy and science are not opposed. Third, tradition-driven chaplaincy already utilizes medical evidence. Fourth, spirituality is the domain of chaplaincy and other health care disciplines have provided the research in our domain. However, if chaplaincy becomes more scientific, it does not mean that chaplaincy will maintain or grow in its position in health care reform. Health care reform in relation to chaplaincy is driven more by values than evidence.
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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 | 0.126 | 0.361 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.060 |
| Scholarly communication | 0.018 | 0.041 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.104 | 0.132 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".