Bibliographic record
Abstract
Spirituality is increasingly recognized as an essential element of health. A novel model of interprofessional spiritual care was developed by a national consensus conference of experts in spiritual care and palliative care. Integral to this model is a spiritual screening, history or assessment as part of the routine history of patients. Spiritual screening can be done by a clinician on an intake into a hospital setting. Clinicians who make diagnosis and assessments and plans, and make referrals to appropriate experts do spiritual histories. In spiritual care, board certified chaplains, spiritual directors and pastoral counselors are the typical spiritual care referrals. Board certified chaplain do a spiritual assessment that is a more detailed assessment of religious and spiritual beliefs and how those impact care or patient's healthcare decision-making. There are several screening and history tools. One history tool named FICA, was developed by a group of primary care physicians and recently validated at study at the City of Hope. This tool is widely used in a variety of clinical settings in the US and Canada. The spiritual history tools allow the clinician the opportunity to diagnose spiritual distress or identify patients' spiritual resources of strength and then integrate that information into the clinical treatment or care plan.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 0.007 |
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".