Bibliographic record
Abstract
Objective: Measurement of outcomes is important, providing accurate and useful information to consumers, service-providers, managers, policymakers and researchers. Assessment tools evaluate particular attributes of the individual and should have sound psychometric properties. The International Classification of Functioning, Disability and Health (ICF) has recently been developed and endorsed by the World Health Organization, and provides an extremely useful framework for selecting appropriate assessment tools. All aspects of a child's health and functioning at the organ system, individual and societal levels are considered. Furthermore, possible facilitators and obstacles to achieving functional independence that are either intrinsic to the child (personal factors) or extrinsic (environmental factors) are considered. Common assessment tools used in the clinical setting and in clinical research for children and youth with cerebral palsy will be briefly described. In particular, assessments such as the Gross Motor Function Measure and the Gross Motor Function Classification System and new quality of life measures developed specifically for this population of interest will be highlighted. Careful consideration of all levels of functioning and health and their determinants may be helpful in guiding program planning, interventions and health policy so as to optimize functional outcomes. In particular, key contextual factors such as community resources, family supports and the child's motivation are potentially modifiable, and therefore efforts to address personal and environmental obstacles may ultimately maximize a child's intrinsic functional potential. Current challenges to the adoption of a more holistic, client-centred approach to the evaluation of children's functioning and health will be a focus of this presentation.
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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 | 0.010 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.009 |
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".