Improving the quality of care delivered to adolescents in Europe: a time to invest
Bibliographic record
Abstract
While many governments, non governmental organisations (NGOs) and United Nations (UN) agencies have focused in the past on the health of mothers, infants and young children, there is now growing evidence that the healthcare system should also address the well-being and problems of adolescents , defined by WHO as individuals aged 10–19 years. They represent 1.2 billion individuals in the global population and between 10% and 25% of the population in European countries.1 In September 2015, the UN Secretary-General announced that the ‘Every Woman, Every Child’ agenda would move forward to 2030 as a Global Strategy for Women’s, Children’s and Adolescents’ Health. In 2017, WHO responded to the large number of health problems affecting adolescents by launching a state-of-the-art review of programmes and interventions targeting the health burden of adolescents around the world, the AA-HA initiative (‘Accelerated Action for the Health of Adolescents’). Adolescents’ morbidities such as sexually transmitted infections or unplanned pregnancies, intentional and unintentional injuries, substance abuse and chronic disorders, especially mental disorders and metabolic diseases, constitute major causes of adolescent ill-health and have both short-term and long-term consequences.2 Among the many stakeholders who need to address the issues of adolescent health (eg, policy makers, professionals in charge of environmental measures or preventive interventions in the school and the community), healthcare professionals have an important role to play. For several decades, countries such as the USA, Canada and Australia have recognised the specific needs of adolescents and have thus developed dedicated healthcare structures and a specific area of training in the field,3–5 while Europe is still lagging behind.6 7 From the viewpoint of international standards, the quality of healthcare currently delivered to adolescents in Europe is less than optimal. The objective of this paper is to examine options for improving the quality of care delivered to adolescents, …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".