Discourses of compassion from the margins of health care: the perspectives and experiences of people with a mental health condition
Bibliographic record
Abstract
BACKGROUND: Evidence supports the positive influence of compassion on care experiences and health outcomes. However, there is limited understanding regarding how compassion is identified by people with lived experience of mental health care. AIM: To explore the views and experiences of compassion from people who have lived experience of mental health. METHODS: 10) were interviewed in a community setting. Characteristics of compassion were identified using an interpretative description approach. RESULTS: Study participants identified compassion as comprised three key components; 'the compassionate virtues of the healthcare professional', which informs 'compassionate engagement', creating a 'compassionate relational space and the patient's felt-sense response'. When all these elements were in place, enhanced recovery and healing was felt to be possible. Without the experience of compassion, mental health could be adversely affected, exacerbating mental health conditions, and leading to detachment from engaging with health services. CONCLUSIONS: The experience of compassion mobilises hope and promotes recovery. Health care policymakers and organisations must ensure services are structured to provide space and time for compassion to flourish. It is imperative that all staff are provided with training so that compassion can be acquired and developed.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.021 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".