Decision‐making and future planning for children with life‐limiting conditions: a qualitative systematic review and thematic synthesis
Bibliographic record
Abstract
BACKGROUND: In the last decade, the number of children with life-limiting and life-threatening conditions in England has almost doubled, and it is estimated that worldwide, there are 1.2 million children with palliative care needs. Families and professionals caring for children with life-limiting conditions are likely to face a number of difficult treatment decisions and develop plans for future care over the course of the child's life, but little is known about the process by which these decisions and plans are made. METHODS: The purpose of this review is to synthesize findings from qualitative research that has investigated decision-making and future planning for children with life-limiting conditions. A systematic search of six online databases was conducted and identified 887 papers for review; five papers were selected for inclusion, using predefined criteria. Reference list searching and contacting authors identified a further four papers for inclusion. RESULTS: Results sections of the papers were coded and synthesized into themes. Nineteen descriptive themes were identified, and these were further synthesized into four analytical themes. Analytical themes were 'decision factors', 'family factors', 'relational factors' and 'system factors'. CONCLUSIONS: Review findings indicate that decision-making and future planning is difficult and needs to be individualized for each family. However, deficits in understanding the dynamic, relational and contextual aspects of decision-making remain and require further research.
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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 | 0.043 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".