Improving the wellbeing of staff who work in palliative care settings: A systematic review of psychosocial interventions
Bibliographic record
Abstract
BACKGROUND: Staff in palliative care settings perform emotionally demanding roles which may lead to psychological distress including stress and burnout. Therefore, interventions have been designed to address these occupational risks. AIM: To investigate quantitative studies exploring the effectiveness of psychosocial interventions that attempt to improve psychological wellbeing of palliative care staff. DESIGN: A systematic review was conducted according to methodological guidance from UK Centre for Reviews and Dissemination. DATA SOURCES: A search strategy was developed based on the initial scans of palliative care studies. Potentially eligible research articles were identified by searching the following databases: CINAHL, MEDLINE (Ovid), PsycINFO and Web of Science. Two reviewers independently screened studies against pre-set eligibility criteria. To assess quality, both researchers separately assessed the remaining studies using the Quality Assessment Tool for Quantitative Studies. RESULTS: A total of 1786 potentially eligible articles were identified - nine remained following screening and quality assessment. Study types included two randomised controlled trials, two non-randomised controlled trial designs, four one-group pre-post evaluations and one process evaluation. Studies took place in the United States and Canada (5), Europe (3) and Hong Kong (1). Interventions comprised a mixture of relaxation, education, support and cognitive training and targeted stress, fatigue, burnout, depression and satisfaction. The randomised controlled trial evaluations did not improve psychological wellbeing of palliative care staff. Only two of the quasi-experimental studies appeared to show improved staff wellbeing although these studies were methodologically weak. CONCLUSION: There is an urgent need to address the lack of intervention development work and high-quality research in this area.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".