Palliative psychiatry: research, clinical, and educational priorities
Bibliographic record
Abstract
BACKGROUND: Palliative psychiatry has been proposed as a new clinical construct within mental health care and aims to improve quality of life (QoL) for individuals experiencing severe and persistent mental illness (SPMI). To date, explorations of palliative psychiatry have been largely theoretical, and more work is needed to develop its approaches into tangible clinical practice. METHODS: In this paper, we synthesize existing literature with discussions held at a one-day knowledge user meeting titled "A Community of Practice for Palliative Psychiatry" to generate priorities for research, clinical practice, and education that will help advance the development of palliative psychiatry. RESULTS: Palliative psychiatry will benefit from research that is co-produced by people with lived experience (PWLE) of mental illness, that clarifies contested concepts within mental health care and wider medicine, and that adapts existing interventions that have the potential to improve the QoL of individuals experiencing SPMI into the mental health care context. Specific methods and tools might be developed for use in clinical spaces taking a palliative psychiatry approach. More work must be done to understand the populations that might benefit from palliative psychiatry, and to mitigate mental health care providers' (MHCPs') anxieties about using these approaches in their work. As palliative psychiatry is developed, current MHCPs, trainees, individuals experiencing SPMI, and their loved ones will all require education about and orientation to this novel approach within mental health care. CONCLUSIONS: There are several priorities in research, clinical practice, and education that can help advance the development of palliative psychiatry. All future work must be considered through a human rights-based, anti-oppressive lens. Research projects, clinical models, and educational initiatives should all be developed in co-production with PWLE to mitigate the epistemic injustices common in mental health 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 | 0.062 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.018 | 0.018 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".