Abstracts presented at the 23rd IAAH European Regional Conference “Adolescent health and well-being - Hope in a time of uncertainty” October 10 <sup>th</sup> -11 <sup>th</sup> , 2024, Copenhagen, Denmark
Bibliographic record
Abstract
Background/Aim: Cycles of school closure and reopening during the COVID-19 pandemic impacted the daily life and well-being of young people. In summer 2020, during the first global wave, the United Nations (UN) H6+ Technical Working Group on Adolescent Health and Wellbeing published a conceptual framework, with 5 domains – 1 good health, 2 connectedness, 3 safety, 4 education and 5 agency. The aim of this abstract, within a wider study of safe school re-opening, is to explore the framework’s utility for exploring adolescent well-being during this period. Methods: In 2021, online semi-structured interviews were conducted in six languages with education and health professionals. They explored the: (i) effect of the pandemic on schools, pupils and teachers; (ii) reorganisation of schools (iii) experience of implementing infection control measures in schools; (iv) intersectoral working; (v) important resources for keeping schools open. Interviews were transcribed verbatim and translated into English where needed. Deductive qualitative analysis was undertaken for all 5 domains of the framework. Results: Sixty-two interviews were included in the analysis (22 health and 40 education professionals from 28 countries). Participant perspectives related to all five well-being domains, but mainly Domain 1 (good health and nutrition), Domain 3 (safety and a supportive environment) and Domain 4 (learning, competence, education, skills and employability). Reflections of 2-connectedness and 5-agency were present, if not plentiful. Discussion/Conclusions: The adolescent well-being framework provided a useful structure for deductive analysis, and data were identified within all domains. The emphasis on health, safety and learning reflects the context.
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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 | 0.015 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".