Developing a comprehensive ICF core set to document health and functioning in children with Cancer in India: A preliminary study
Bibliographic record
Abstract
BACKGROUND: This study aimed to develop a comprehensive ICF Core Set for children with cancer to facilitate the documentation of their health and functioning. Two preparatory phase studies were conducted to gather perspectives from caregivers and healthcare professionals. METHODS: Semi-structured interviews with caregivers and a cross-sectional survey with healthcare professionals were conducted to understand the challenges children face due to cancer and its treatment, as well as environmental factors impacting their functioning. Interviews were audio-recorded, transcribed verbatim, and analyzed qualitatively. The themes derived from the analysis were coded and linked to the ICF framework using Cieza linking rules. RESULTS: A comprehensive ICF Core Set comprising 156 categories was formulated by combining responses from 21 healthcare professionals and 18 caregivers. Categories were distributed across the body structure, body functions, activities/participation, and environmental factors domains. Specifically, 40 categories related to body structure, 65 to body functions, 23 to activities/participation, and 27 to environmental factors. CONCLUSION: The use of the ICF to develop the Core Set is significant as it provides a multidimensional perspective on the health and functioning of children with cancer. Incorporating intrinsic and extrinsic factors, the Core Set can assist multidisciplinary treatment teams in delivering individualized and holistic care to pediatric patients. Additionally, it can serve as a foundation for standardizing assessment tools and planning preventative and intervention strategies for this population.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".