Measurement of psychological distress in palliative care
Bibliographic record
Abstract
Research investigating the psychological distress of palliative care patients has contributed to our understanding of the needs and experiences of individuals approaching death. This paper aims to provide a brief review of such measurement of psychological distress in palliative care, focusing on established psychiatric and psychological research tools, and quantitative research methods. This includes clinical screening and diagnostic assessment instruments used to identify key distress-related symptoms and the presence of common clinical syndromes, such as depression, anxiety, delirium, as well as the broader psychological dimensions of suffering, such as existential concerns, spirituality, hope and demoralisation. There are important considerations in undertaking psychological research in palliative care, such as maintaining a balance between the methods and measurements that will address key research questions, and sensitivity to the range of physical and emotional demands facing individuals at the point of receiving palliative care. The clinical application of psychological and psychiatric research tools and methods can aid the detection of psychological distress, aid the thorough assessment of the psychological dimension of the patients' illness and care, aid the identification of individuals who would benefit from specific psychotherapeutic or pharmacologic interventions, and the evaluation of response to treatments.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".