Supportive Care Needs of Parents of Children With Cancer: Transition From Diagnosis to Treatment
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: To analyze research related to the pediatric oncology population supportive care needs from diagnosis to treatment. DATA SOURCES: Articles published from 1992-2002. DATA SYNTHESIS: 49 studies were included. All six categories of the Supportive Care Needs Framework were found, with most studies addressing one to three of the need categories. Informational (88%) and emotional (84%) needs were identified most frequently. CONCLUSIONS: No one study examined the entire range and types of supportive care needs from diagnosis to treatment. This knowledge is key to planning appropriate care and services. Future research should be directed at understanding the full constellation of needs encountered by parents during this time. Further refinement of the Supportive Care Needs Framework is required to fully define the categories of need. IMPLICATIONS FOR NURSING: Although more research is required, supportive care that focuses on informational and emotional support appears to be most important from diagnosis to treatment. Using a conceptual framework such as the Supportive Care Needs Framework provides a methodology for planning care based on needs.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".