Emotional wellbeing as a determinant of flourishing: insights from adolescents in three communities in Uganda
Bibliographic record
Abstract
This study investigates the understanding of emotional well-being among adolescents aged 14–19. Recognising the limited data, especially for younger adolescents, regarding their mental and emotional well-being, resilience, and coping mechanisms, this research aims to understand how adolescents themselves perceive their emotional well-being. This perspective is crucial for a more comprehensive understanding of flourishing in their daily lives, as current international development programmes often overlook adolescents and narrowly define well-being. Employing a mixed-methods approach, the initial quantitative phase involved a cross-sectional study in Uganda's Mpigi and Mayuge Districts. Data were collected from 333 in-school and out-of-school adolescents (14–19 years) between March and April 2023 using electronic surveys. The survey explored seven aspects of flourishing alongside socio-demographic information. The collected data were then cleaned and analysed using descriptive statistics. Additional data from qualitative research were collected in Mpigi, Mayuge and Gombe districts and included interviews with key informants and community members, and photovoice with 30 adolescent participants. This study emphasises the importance of incorporating adolescents' unique perspectives on emotional well-being to inform international development programmes and to advance our understanding of distinctive experiences of flourishing.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".