Access to medical care and its association with physical injury in adolescents: a cross-national analysis
Bibliographic record
Abstract
BACKGROUND: Strong variations in injury rates have been documented cross-nationally. Historically, these have been attributed to contextual determinants, both social and physical. We explored an alternative, yet understudied, explanation for variations in adolescent injury reporting-that varying access to medical care is, in part, responsible for cross-national differences. METHODS: Age-specific and gender-specific rates of medically treated injury (any, serious, by type) were estimated by country using the 2013/2014 Health Behaviour in School-aged Children study (n=209 223). Available indicators of access to medical care included: (1) the Healthcare Access and Quality Index (HAQ; 39 countries); (2) the Universal Health Service Coverage Index (UHC; 37 countries) and (3) hospitals per 100 000 (30 countries) then physicians per 100 000 (36 countries). Ecological analyses were used to relate injury rates and indicators of access to medical care, and the proportion of between-country variation in reported injuries attributable to each indicator. RESULTS: Adolescent injury risks were substantial and varied by country and sociodemographically. There was little correlation observed between national level injury rates and the HAQ and UHC indices, but modest associations between serious injury and physicians and hospitals per 100 000. Individual indicators explained up to 9.1% of the total intercountry variation in medically treated injuries and 24.6% of the variation in serious injuries. CONCLUSIONS: Cross-national variations in reported adolescent serious injury may, in part, be attributable to national differences in access to healthcare services. Interpretation of cross-national patterns of injury and their potential aetiology should therefore consider access to medical care as a plausible explanation.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".