The “F-Words” in Pediatric Oncology: Improving Pediatric Cancer Care Through Innovative Thinking and Rehabilitation for Optimal Quality of Life
Bibliographic record
Abstract
Conceptual frameworks inform and advance clinical practice, research, and education. The “F-words for child development” is a child/youth/family friendly model that embeds the words fitness, functioning, family, friends, fun, and future into the International Classification of Functioning, Disability and Health (ICF). This perspectives paper explores how the concepts and use of the F-words for child development are conducive to the practice of pediatric oncology rehabilitation and promotion of quality of life of children and their families. Pediatric oncology rehabilitation is explored in the context of the ICF and F-words through a review of relevant literature, clinical expertise, and child/youth/family perspectives. The F-words framework is congruent with recognized rehabilitation strategies, preferences, and best practices during and after treatment for cancer in children and youth. The concepts can be used to promote the engagement, health, well-being, and quality of life of children/youth and their families.
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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".