Expert Discussion on Taking a Spiritual History
Bibliographic record
Abstract
This article elaborates on the hazards of spiritual history taking. It provides expert insights to consider before entering the field. In summer 2012, a group of spiritual care experts were invited to discuss the complexity of taking spiritual histories in a manner of hermeneutic circle. Thematic analysis was applied to define the emerging themes. The results demonstrate that taking a spiritual history is a complex and challenging task, requiring a number of personal qualities of the interviewer, such as 'being present', 'not only hearing, but listening', 'understanding the message beyond the words uttered', and 'picking up the words to respond'. To 'establish a link of sharing', the interviewer is expected 'to go beyond the ethical stance of neutrality'. The latter may cause several dilemmas, such as 'fear of causing more problems', 'not daring to take it further', and above all, 'being ambivalent about one's role'. Interviewer has to be careful in terms of the 'patient's vulnerability'. To avoid causing harm, it is essential to propose 'a follow-up contract' that allows responding to 'patient's yearning for genuine care'. These findings combined with available literature suggest that the quality of spiritual history taking will remain poor unless the health-care professionals revise the meaning of spirituality and the art of caring on individual level.
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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 | 0.087 | 0.219 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.020 | 0.016 |
| Insufficient payload (model declined to judge) | 0.014 | 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".