<i>Caring Decisions:</i> The Development of a Written Resource for Parents Facing End-of-Life Decisions
Bibliographic record
Abstract
BACKGROUND: Written resources in adult intensive care have been shown to benefit families facing end of life (EoL) decisions. There are few resources for parents making EoL decisions for their child and no existing resources addressing ethical issues. The Caring Decisions handbook and website were developed to fill these gaps. AIM: We discuss the development of the resources, modification after reviewer feedback and findings from initial pilot implementation. DESIGN: A targeted literature review-to identify resources and factors that impact on parental EoL decision-making; development phase-guided by the literature and the researchers' expertise; consultation process-comprised a multi-disciplinary panel of experts and parents; pilot evaluation study-hard-copy handbook was distributed as part of routine care at an Australian Children's Hospital. SETTING/PARTICIPANTS: Twelve experts and parents formed the consultation panel. Eight parents of children with life-limiting conditions and clinicians were interviewed in the pilot study. RESULTS: Numerous factors supporting/impeding EoL decisions were identified. Caring Decisions addressed issues identified in the literature and by the multidisciplinary research team. The consultation panel provided overwhelmingly positive feedback. Pilot study parents found the resources helpful and comforting. Most clinicians viewed the resources as very beneficial to parents and identified them as ideal for training purposes. CONCLUSIONS: The development of the resources addressed many of the gaps in existing resources. The consultation process and the pilot study suggest these resources could be of significant benefit to parents and clinicians.
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 | 0.017 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".