Empowering patients of a mental rehabilitation center in a low-resource context: a Moroccan experience as a case study
Bibliographic record
Abstract
Mental, neurological and substance use (MNS) disorders represent a major source of disability and premature mortality worldwide. However, in developing countries patients with MNS disorders are often poorly managed and treated, particularly in marginalized, impoverished areas where the mental health gap and the treatment gap can reach 90%. Efforts should be made in promoting help by making mental health care more accessible. In this article, we address the challenges that psychological and psychiatric services have to face in a low-resource context, taking our experience at a Moroccan rehabilitation center as a case study. A sample of 60 patients were interviewed using a semi-structured questionnaire during the period of 2014-2015. The questionnaire investigated the reactions and feelings of the patients to the rehabilitation program, and their perceived psychological status and mental improvement, if any. Interviews were then transcribed and processed using ATLAS.ti V.7.0 qualitative analysis software. Frequencies and co-occurrence analyses were carried out. Despite approximately 30 million inhabitants within the working age group, Morocco suffers from a shortage of specialized health workers. Our ethnographic observations show that psychiatric treatment can be ensured, notwithstanding these hurdles, if a public health perspective is assumed. In resource-limited settings, working in the field of mental health means putting oneself on the line, exposing oneself to new experiences, and reorganizing one's own skills and expertise. In the present article, we have used our clinical experience at a rehabilitation center in Fes as a case study and we have shown how to use peer therapy to overcome the drawbacks that we are encountered daily in a setting of limited resources.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".