What we know (and do not know) about raising children with complex continuing care needs
Bibliographic record
Abstract
In western industrialized countries, long-term life-support technologies such as ventilators and nutrition systems allow many children to survive diseases and injuries previously considered fatal.Most are cared for in their homes, where they are cherished members of their families.These children have complex continuing care needs that stem from multi-organ system involvement and cognitive and/or developmental problems.Incidence and prevalence rates are unknown because different terms are used to classify this small but growing pediatric population.Each of us has conducted research with these families in Canada, the US and UK respectively.Although these countries have different models of health and social service provision, our findings are similar.Our combined findings corroborate those of other researchers, indicating that radically new forms of childhood, parenthood and family life have been created, but are poorly understood.We know that these children commonly receive sub-optimal long-term care because they 'fall between the cracks' due to ambiguous categories, exclusionary criteria or service gaps.Nevertheless, most parents emphasize the important benefits that they derive from raising them and the enhancements they make to family life.However, daily life is extremely constrained also by extraordinary physical, psychological, social and financial challenges.
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.017 | 0.030 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".