Building Bridges: An Interpretive Phenomenological Analysis of Nurse Educators' Clinical Experience Using the T.R.U.S.T. Model for Inclusive Spiritual Care
Bibliographic record
Abstract
Educating nurses to provide evidence-based, non-intrusive spiritual care in today's pluralistic context is both daunting and essential. Qualitative research is needed to investigate what helps nurse educators feel more prepared to meet this challenge. This paper presents findings from an interpretive phenomenological analysis of the experience of nurse educators who used the T.R.U.S.T. Model for Inclusive Spiritual Care in their clinical teaching. The T.R.U.S.T. Model is an evidence-based, non-linear resource developed by the author and piloted in the undergraduate nursing program in which she teaches. Three themes are presented: "The T.R.U.S.T. Model as a bridge to spiritual exploration"; "blockades to the bridge"; and "unblocking the bridge". T.R.U.S.T. was found to have a positive influence on nurse educators' comfort and confidence in the teaching of spiritual care. Recommendations for maximizing the model's positive impact are provided, along with "embodied" resources to support holistic teaching and learning about spiritual 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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.016 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".