Highlighting successes and challenges of the mental health system in Tunisia: an overview of services, facilities, and human resources
Bibliographic record
Abstract
BACKGROUND: Tunisia is a lower-middle-income country located in North Africa with strengths and challenges to its mental health system. AIMS: We present an overview of available services, facilities, and human resources to offer mental health care in Tunisia. METHODS: We conducted a cross-sectional study, where data for the year 2017 was collected between May 2018 and May 2019 by consulting stakeholders involved in the health field in Tunisia. We compare this information with data published in the WHO-AIMS report (2008), which presents mental health data in Tunisia for the year 2004. RESULTS: Successes of the mental health system in Tunisia include an increase in the ratios of psychiatrists and psychologists, with these ratios being higher than those of other lower-middle-income countries; a flourishing child-psychiatry practice; and an increase in people being treated for mental health conditions. Challenges include psychiatrists being over-represented in large cities along the coastline and in the private sector, and most people receiving treatment in specialized mental health facilities. CONCLUSIONS: The further operationalization of the National Strategy for Mental Health Promotion is envisioned, through the training of non-specialists in mental health care and incentives offered to psychiatrists to work in the country's interior and the public sector.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".