Drivers of youth engagement in mental health in Morocco: findings from a nationwide cross-sectional survey
Bibliographic record
Abstract
Abstract Background Youth engagement in mental health has been shown to inform effective interventions aimed at improving youth mental health outcomes. However, evidence on the state of youth engagement in mental health remains limited in low- and middle-income countries (LMICs). This study aims to identify the drivers of youth engagement in mental health in Morocco, as well as the support needs and resources required to promote it. Methods We conducted a nationwide cross-sectional study in Morocco, including young Moroccans aged 18–24 years. Using an online self-administered questionnaire, we assessed participants’ levels of engagement in mental health activities, attitudes toward mental health, awareness about mental health, support needs, and perceived importance of well-being drivers across five domains, informed by the WHO adolescent well-being framework. Descriptive statistics, clustering analysis, and logistic regression analyses were used to identify predictor factors of youth engagement in mental health. Results A total of 1,183 participants were included. The engaged cluster group reported higher awareness about mental health and more positive attitudes toward mental health. Predictors of engagement in mental health included higher education (OR=2.23, 95% CI: [46-3.43]) unemployment (OR=1.65, 95% CI: [1.04-2.64]), and higher scores on “Connectedness and positive values and contribution to society” (OR=1.07, 95% CI: [1.05-1.10]) as well as positive attitudes toward mental health (OR=1.04, 95% CI: [1.03-1.05]). Conversely, those who prioritized ‘safety and a supportive environment’ were less likely to be engaged (OR=0.94, 95% CI: [0.91-0.98]). The most frequently cited needs to support engagement were access to mental health professionals (63.0%) and mental health education (42.4%). Conclusions These results provide insights into the factors influencing youth engagement in mental health in Morocco. Fostering “connectedness, positive values, and contribution to society” and “positive attitudes towards mental health”, as well as improving access to mental health professionals, information and education, is essential to promote youth engagement in mental health programs and policy-making.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| 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".