Mental health knowledge, attitudes, and self-efficacy among primary care physicians working in the Greater Tunis area of Tunisia
Bibliographic record
Abstract
Non-specialists’ involvement in mental health care is encouraged in the field of global mental health to address the treatment gap caused by mental illness, especially in low- and middle-income countries. While primary care physicians (PCPs) are involved in mental health care in Tunisia, a lower-middle-income country in North Africa, it is unclear to what extent they are prepared and willing to address mental health problems, substance use disorders, and suicide/self-harm. In this context, we aim (1) to report on mental health knowledge, attitudes, and self-efficacy among a sample of PCPs working in the Greater Tunis area, prior to the implementation of a mental health training program developed by the World Health Organization ; and (2) to identify what characteristics are associated with these competencies. In total, 112 PCPs completed questionnaires related to their socio-demographic and practice characteristics, as well as their mental health knowledge, attitudes, and self-efficacy. Descriptive analyses and regression models were performed. PCPs had more knowledge about depression, symptoms related to psychosis, and best practices after a suicide attempt; had favourable attitudes about distinctions between physical and mental health, learning about mental health, and the acceptance of colleagues with mental health issues; and believed most in their capabilities related to depression and anxiety. However, most PCPs had less knowledge about substance use disorders and myths about suicide attempts; had unfavorable attitudes about the dangerousness of people with mental health problems, personal disclosure of mental illness, non-specialists’ role in assessing mental health problems, and personal recovery; and believed the least in their capabilities related to substance use disorders, suicide/self-harm, and psychosis. Participation in previous mental health training, weekly hours (and weekly hours dedicated to mental health), weekly provision of psychoeducation, and certain work locations were associated with better mental health competencies, whereas mental health knowledge was negatively associated with weekly referrals to specialized services. Findings suggest that PCPs in our sample engage in mental health care, but with some gaps in competencies. Mental health training and increased interactions/involvement with people consulting for mental health issues may help further develop non-specialists’ mental health competencies, and integrate mental health into primary care settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".