S2-2 Supporting the integration of spiritual care as an essential component of palliative care
Bibliographic record
Abstract
Spirituality and spiritual care are important components of high-quality palliative care from the time of diagnosis through the end of life (Zumstein-Shaha, Ferrell, & Economou, 2020). Research has shown that nurses play a key role in helping patients identify their spiritual needs and in responding to those needs. Nurses have found that supporting patients in their spiritual beliefs and helping them find meaning in their illness leads to disease acceptance (Zumstein-Shaha, Ferrell, & Economou, 2020). Thus, it is important for nurses to feel prepared to participate in spiritual care and for spirituality to be integrated into palliative care delivery for all members of the healthcare team. This session will describe a project that supports the integration of spiritual care as a component of quality palliative care through the lifespan. The project is the End-of-Life Nursing Education Consortium (ELNEC), which is a national and international education initiative to improve palliative care that provides education to nurses, advanced practice nurses, and other professionals in a train-the-trainer model so they can teach this essential information to nursing students, practicing nurses, advanced practice nurses, and other healthcare professionals at their own institutions. ELNEC was started in 2000 and over 1.4 million nurses and other healthcare professionals, representing all 50 US states, plus 101 international countries have completed a national or international ELNEC train-the-trainer course since that time. (www.aacnnursing.org/ELNEC) Spirituality is a component of the ELNEC curriculum and will be used as an example of how to promote spiritual care as an obligation for all disciplines. This session will highlight the importance of integrating spiritual care as an essential component of palliative care and will provide an example of how a nursing education model includes this type of care.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.423 | 0.112 |
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".