Conceptualizing childhood health problems using survey data: a comparison of key indicators
Bibliographic record
Abstract
BACKGROUND: Many definitions are being used to conceptualize child health problems. With survey data, commonly used indicators for identifying children with health problems have included chronic condition checklists, measures of activity limitations, elevated service use, and health utility thresholds. This study compares these different indicators in terms of the prevalence rates elicited, and in terms of how the subgroups identified differ. METHODS: Secondary data analyses used data from the National Longitudinal Survey of Children and Youth, which surveyed a nationally representative sample of Canadian children (n = 13,790). Descriptive analyses compared healthy children to those with health problems, as classified by any of the key indicators. Additional analyses examined differences between subgroups of children captured by a single indicator and those described as having health problems by multiple indicators. RESULTS: This study demonstrates that children captured by any of the indicators had poorer health than healthy children, despite the fact that over half the sample (52.2%) was characterized as having a health problem by at least one indicator. Rates of child ill health differed by indicator; 5.6% had an activity limitation, 9.2% exhibited a severe health difficulty, 31.7% reported a chronic condition, and 36.6% had elevated service use. Further, the four key indicators captured different types of children. Indicator groupings differed on child and socio-demographic factors. Compared to children identified by more than one indicator, those identified only by the severe health difficulty indicator displayed more cognitive problems (p < 0.0001), those identified only by the chronic condition checklist had a greater likelihood of reporting allergies or asthma (p < 0.0001), and those identified as having elevated service use only were more affluent (p = 0.01) and showed better overall health (p < 0.0001). Children identified by only a single indicator were less likely to have serious health problems than those identified by two or more indicators. CONCLUSION: We provide information useful to researchers when selecting indicators from survey data to identify children with health problems. Researchers and policy makers need to be aware of the impact of such definitions on prevalence rates as well as on the composition of children classified as being in poor health.
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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 | 0.043 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".