Barriers and enablers to help-seeking for common mental disorders among young people in low-income settings: Perspectives from Zimbabwe
Bibliographic record
Abstract
Common Mental Disorders (CMDs), such as depression and anxiety are highly prevalent, particularly among young people globally. In Zimbabwe, contributing factors like poverty, unemployment, and the COVID-19 pandemic have exacerbated these challenges. Despite the pressing need for mental health support among young people, there remains a significant knowledge gap on barriers and enablers to help-seeking for CMDs among this demographic. This study addressed this gap by applying the Consolidated Framework for Implementation Research (CFIR) as an analytical framework to explore the unique factors influencing mental health help-seeking among young people in Zimbabwe. Methods We utilised a qualitative research design and conducted 32 semi-structured interviews with young people (15–24 years) across high schools and the Friendship Bench (FB) in Harare between 20 December 2022 and 30 September 2023. Interviews were audiotaped and transcribed verbatim and then coded using an inductive approach to capture patterns grounded in participants’ experiences. Thematic analysis was utilised to develop relevant codes and identify relevant themes. Results Nine themes were generated including six themes related to barriers (factors that hinder help-seeking for CMDs) and three themes related to enablers (factors that facilitate help-seeking for CMDs). Barriers identified include perceived stigma, privacy and confidentiality issues, unavailability of services, lack of awareness, financial challenges and lack of incentives. Enablers identified include raising awareness, implementing school based initiatives and enhancing accessibility and affordability of mental health services. Conclusion This study revealed significant barriers and enablers to help-seeking for CMDs among young people in Zimbabwe. Addressing these multifaceted barriers and leveraging the identified enablers is key to creating supportive systems that encourage young people in low-resource settings to seek and engage with mental health services, ultimately improving their mental wellbeing and overall quality of life.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".