Healthcare staff ’s experience in providing end-of-life care to children: A mixed-method review
Bibliographic record
Abstract
BACKGROUND: Staff who provide end-of-life care to children not only have to deal with their own sense of loss but also that of bereaved families. There is a dearth of knowledge on how they cope with these challenges. AIM: The aim of this review is to explore the experiences of healthcare professionals who provide end-of-life care to children in order to inform the development of interventions to support them, thereby improving the quality of paediatric care for both children and their families. DATA SOURCES: Searches included CINAHL, MEDLINE, Web of Science, EMBASE, PsychINFO and The Cochrane Library in June 2015, with no date restrictions. Additional literature was uncovered from searching reference lists of relevant studies, along with contacting experts in the field of paediatric palliative care. DESIGN: This was a systematic mixed studies review. Study selection, appraisal and data extraction were conducted by two independent researchers. Integrative thematic analysis was used to synthesise the data. RESULTS: The 16 qualitative, 6 quantitative and 8 mixed-method studies identified included healthcare professionals in a range of settings. Key themes identified rewards and challenges of providing end-of-life care to children, the impact on staff's personal and professional lives, coping strategies and key approaches to help support staff in their role. CONCLUSION: Education focusing on the unique challenges of providing end-of-life care to children and the importance of self-care, along with timely multidisciplinary debriefing, are key strategies for improving healthcare staff's experiences, and as such the quality of care they provide.
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 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 | 0.036 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".