Supporting diversity in person-centred care: The role of healthcare chaplains
Bibliographic record
Abstract
AIM: To explore healthcare chaplains' experience of providing spiritual support to individuals and families from minority religious and non-religious faiths and to identify key elements of the role. BACKGROUND: Currently, there is limited research uncovering the essential elements of healthcare chaplaincy, specifically with reference to religious and/or spiritual diversity, and as interprofessional collaborators with nurses and midwives in healthcare. RESEARCH DESIGN AND PARTICIPANTS: Using phenomenology, we interviewed eight healthcare chaplains from a variety of healthcare settings in the Republic of Ireland. Data were analysed using a seven-step framework comprising Moustakas' (1994) modification of the Van Kaam method of data analysis. ETHICAL CONSIDERATIONS: Ethical approval was granted by the university and the principles of informed consent applied. FINDINGS: Three main themes emerged: what the chaplain brings; components of ritual, minority faith or no faith; and practising chaplaincy. Subthemes included 'offering', 'awareness and insight', 'acceptance and empathy', 'skilled companionship', 'presence', 'a confidant and holder of hope' and 'a vital resource'. DISCUSSION AND CONCLUSIONS: The healthcare chaplain is a key collaborator in facilitating holistic person-centred care and in supporting healthcare professionals. Chaplaincy services are an essential but largely unrecognised and potentially cost-effective component of interprofessional team working. RELEVANCE TO CLINICAL PRACTICE: This study has illuminated key aspects of the healthcare chaplain's role as interprofessional collaborator in person-centred care, in navigating diversity and ensuring respect and dignity for the person irrespective of religious or spiritual care needs.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".