Compassion in pediatric oncology: A patient, parent and healthcare provider empirical model
Bibliographic record
Abstract
OBJECTIVE: Compassion has long been considered a cornerstone of quality pediatric healthcare by patients, parents, healthcare providers and systems leaders. However, little dedicated research on the nature, components and delivery of compassion in pediatric settings has been conducted. This study aimed to define and develop a patient, parent, and healthcare provider informed empirical model of compassion in pediatric oncology in order to begin to delineate the key qualities, skills and behaviors of compassion within pediatric healthcare. METHODS: Data was collected via semi-structured interviews with pediatric oncology patients (n = 33), parents (n = 16) and healthcare providers (n = 17) from 4 Canadian academic medical centers and was analyzed in accordance with Straussian Grounded Theory. RESULTS: Four domains and 13 related themes were identified, generating the Pediatric Compassion Model, that depicts the dimensions of compassion and their relationship to one another. A collective definition of compassion was generated-a beneficent response that seeks to address the suffering and needs of a person and their family through relational understanding, shared humanity, and action. CONCLUSIONS: A patient, parent, and healthcare provider informed empirical pediatric model of compassion was generated from this study providing insight into compassion from both those who experience it and those who express it. Future research on compassion in pediatric oncology and healthcare should focus on barriers and facilitators of compassion, measure development, and intervention research aimed at equipping healthcare providers and system leaders with tools and training aimed at improving it.
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 | 0.006 | 0.013 |
| 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 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".