Attitudes of Jordanian Medical Students Toward People With Mental Illness
Bibliographic record
Abstract
Background For people with mental illnesses, the stigma in the healthcare system and among healthcare providers has been identified as a major barrier to accessing treatment and achieving recovery, as well as poor physical treatment. This nationwide cross-sectional study assessed stigma toward mental illnesses among medical students in Jordan. This study aimed to assess the prevalence and patterns of stigma toward mental illnesses among medical students in Jordan and examine the influence of psychiatric education and specialty preferences on stigma levels. Methodology A nationwide cross-sectional study was conducted between April 21 and April 30, 2021, targeting medical students from all six medical schools in Jordan. Participants completed a validated, structured, electronically distributed questionnaire assessing attitudes toward mental illness. Exploratory factor analysis (EFA) identified three key domains, i.e., (1) Social Acceptance, (2) Policies to Promote Community Integration, and (3) Beliefs about Treatability of Mental Illness. Demographic information included age, academic year (pre-clinical vs. clinical), and psychiatric training exposure (classroom and/or clinical). Data analysis was performed using SPSS (IBM Corp., Armonk, NY, USA). A total of 2,104 medical students participated. The age distribution was as follows: 24.8% were aged 18-19.9 years, 43.6% were aged 20-22.9 years, and 31.7% were aged ≥23 years. Participants included both pre-clinical and clinical-year students from diverse academic institutions across Jordan. Results Clinical-year students reported significantly higher (less stigmatizing) scores in all three domains compared to pre-clinical students (p < 0.001). Those exposed to both classroom and clinical psychiatry training showed more favorable attitudes than those with limited or no exposure. Students interested in psychiatry specialization exhibited lower (more stigmatizing) scores in the treatability and community integration domains compared to peers interested in surgical (p < 0.001, p = 0.003) and medical specialties (p < 0.001, p = 0.001). Conclusions While a majority of students expressed social acceptance and supported community integration of individuals with mental illness, stigma persisted in perceptions of treatability. Greater exposure to psychiatric education correlates with reduced stigma. Unexpectedly, students inclined toward psychiatry showed more stigmatizing views in some domains, warranting further investigation. These findings highlight the importance of enhancing mental health education across the medical curriculum.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".