Working at the Intersection of Palliative End-of-Life and Mental Health Care: Provider Perspectives
Bibliographic record
Abstract
Objective: Palliative, end-of-life care (PEOLC) providers are poorly resourced in addressing the needs of patients with mental health challenges, and the dying experiences of this cohort—particularly those with a comorbid, chronic and persistent mental illness (CPMI)—are poorly documented. We sought to explore the experiences of PEOLC providers with regard to caring for patients with mental health challenges, and gather insights into ways of improving accessibility and quality of PEOLC for these patients. Method: Twenty providers of PEOLC, from different disciplines, took part in semi structured interviews. The data were coded and analyzed using a reflexive, inductive-deductive process of thematic analysis. Results: The most prominent issues pertained to assessment of patients and differential diagnosis of CPMI, and preparedness of caregivers to deliver mental health interventions, given the isolation of palliative care from other agencies. Among the assets mentioned, informal relationships with frontline caregivers were seen as the main support structure, rather than the formal policies and procedures of the practice settings. Strategies to improve mental health care in PEOLC centered on holistic roles and interventions benefiting the entire palliative population, illustrating the participants saw little point in compartmentalizing mental illness, whether diagnosed or not. Conclusions: Continuity of care and personal advocacy can significantly improve quality of life for end-of-life patients with mental health challenges, but bureaucracy and disciplinary siloing tend to isolate these patients and their caregivers. Improved interdisciplinary connectivity and innovative, hybridized roles encompassing palliation and psychiatry are 2 strategies to address this disconnect, as well as enhanced training in core mental health care competencies for PEOLC providers.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.006 |
| 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".