Palliative Professionals’ Experiences of Receiving Gratitude: A Transformative and Protective Resource
Bibliographic record
Abstract
Providing palliative care can be both challenging and rewarding. It involves emotionally demanding work and yet research shows that burnout is lower than in other fields of health care. Spontaneous expressions of gratitude from patients and family members are not uncommon and are highly valued. This study explored the experience of Spanish palliative professionals who received expressions of gratitude from their patients and families. A phenomenological approach was used to better understand the role of receiving gratitude in participants' lives. Interviews were transcribed verbatim and a phenomenological approach to analysis was undertaken using macro-thematic and micro-thematic reflection. Two team members independently engaged in this reflection with an inductive approach. The analysis was shared and discussed at periodic meetings to identify the key themes and sub-themes of the gratitude experience. Ten palliative professionals were interviewed. Participants engaged in a process of recognizing, internalizing, and treasuring the expressions of gratitude which they then used for reflection and growth. These expressions were a powerful and deeply meaningful resource that the palliative professionals revisited over time. Receiving expressions of gratitude invited a stronger sense of the value of one's self and one's work that was motivational and protective, particularly during challenging times.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".