Drivers of youth mental health and wellbeing: A nationwide cross-sectional study in Morocco
Bibliographic record
Abstract
Abstract Background Mental health struggles disproportionately affect young people, particularly in low- and middle-income settings. Understanding key drivers of youth mental health is essential for designing effective policies and programs to close the mental health gap. This study aims to describe the factors influencing mental health and well-being among Moroccan youth. Methods This is a descriptive cross-sectional online survey conducted across 12 regions in Morocco, using stratified random sampling. Moroccan youth aged 18 to 24 were included. Data were collected using a questionnaire informed by and adapted from the WHO Adolescent Wellbeing Framework, distributed through youth-serving organizations. Results A total of 1,182 participants were included (mean age 20.5 years, 68.2% female, 85.7% from urban settings). Regarding health and nutrition, 46.3% valued sleep, 59.7% emphasized physical health, 53.1% highlighted access to quality healthcare, and 56.5% prioritized clean air. In terms of connectedness and contribution, 75.7% rated family relationships as critical to their well-being, while 42.5% emphasized positive peer relationships. Regarding safety and supportive environments, 64.7% considered personal safety essential, 70% prioritized the fulfillment of basic needs, and 63.7% valued personal information protection. For education and competence, 54.4% emphasized learning opportunities and 62.2% identified self-confidence as key drivers. Regarding agency and resilience, 59.4% valued independence, 68.5% stressed having a sense of purpose, and 55% identified hope and optimism as key to their well-being. In digital well-being, 37.7% believed social media helped maintain connections, 38% viewed it as a learning tool, while 31.6% reported it as a source of stress and anxiety. Conclusion This study provides valuable insights into priority drivers of youth mental health in Morocco which should be the target for future interventions aiming to promote youth well-being. The findings contribute to the limited data on youth mental health in LMICs, highlighting the urgency for comprehensive mental health services and further research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".