Indicators Used to Assess the Impact of Specialized Pediatric Palliative Care: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: Specialized pediatric palliative care programs aim to improve quality of life and ease distress of patients and their families across the illness trajectory. These programs require further development, which should be based on how they improve outcomes for patients, families, health care professionals, and the health care system. OBJECTIVE: To identify and compare definitions of indicators used to assess the impact of specialized pediatric palliative care programs. DESIGN: The scoping review protocol was prospectively registered on PROSPERO 2017 (CRD42017074090). DATE SOURCES: MEDLINE, PsycINFO, Cochrane Central Register of Controlled Trials, Web of Science, CINAHL, Scopus, and Embase databases were searched from January 2000 to September 2018. Eligible studies included randomized controlled trials, experimental studies, or observational studies that compared specialized programs with usual care. Studies were excluded if most care recipients were older than 19 years or the article was not available in English, French, German, or Spanish. RESULTS: Forty-six studies were included; one was a randomized controlled trial. We identified 82 different indicators grouped into 14 domains. The most common indicators included the following: location of death, length of stay in hospital, and number of hospital admissions. Only 22 indicators were defined identically in at least 2 studies. Only one study included children's perspectives in assessing indicators. CONCLUSIONS: Many indicators were used to assess program outcomes with little definition consensus across studies. Development of a set of agreed-upon indicators to assess program impact concurrent with family and patient input is essential to advance research and practice in pediatric palliative care.
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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 | 0.055 | 0.200 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.041 | 0.040 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".