Community rehabilitation in childhood: concepts to inform practice
Bibliographic record
Abstract
Introduction Jenna is a 6-year-old girl, the second of two children of a mother who is 29 and her 32-year-old husband. Jenna was born unexpectedly at 30 weeks gestation following an uneventful pregnancy, and experienced a variety of perinatal difficulties requiring a stay in the special care baby unit. Following discharge she was seen regularly at a newborn follow-up clinic, where her developmental progress was monitored carefully. At 8 months Jenna's motor development was ‘slow’ and she appeared somewhat ‘stiff’. At 1 year of age, adjusting for her prematurity, she was thought to have ‘cerebral palsy’, the prognosis of which was uncertain. At 2 years her language was ‘delayed’, but at 3 she had caught up with speech. She entered school at the usual age but needed special help with mobility, and some extra attention for her learning. Throughout this period from birth to age 6 Jenna's family saw a succession of professionals, received extensive but at times conflicting information and advice, and sought the latest news about therapies from the internet. How might one approach the ‘rehabilitation’ needs of Jenna and her family? What are realistic goals, and how should they be achieved? What is Jenna's prognosis, and on what evidence is any judgement based? To understand the ‘rehabilitation’ of children with disabilities professionals need to be familiar with several conceptual underpinnings that distinguish children's needs from those of adults. The purpose of this chapter is to outline and explain these concepts.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".