<p>Nonpsychiatric Healthcare Professionals’ Attitudes Toward Patients with Mental Illnesses in Makkah City, Saudi Arabia: A Cross-Sectional Study</p>
Bibliographic record
Abstract
PURPOSE: Most patients exhibiting psychiatric manifestations often remain undetected, misdiagnosed, and inappropriately managed. This cross-sectional study aims to ascertain the level of knowledge of mental illnesses among nonpsychiatric healthcare workers and their attitudes toward patients with mental illness in Makkah, Saudi Arabia. PATIENTS AND METHODS: A cross-sectional study was conducted in four public hospitals in Makkah from November 2017 to February 2018. A total of 407 participants were involved. A self-reported structured questionnaire was used, and data were collected electronically. RESULTS: Of 407 respondents, 183 (45%) were females and 244 (55%) were males. The majority of respondents were physicians with medical specialties 116 (28.5%), followed by physicians with surgical specialties 99 (24.3%). More than half 229 (56.3%) of the respondents had work experience of >10 years. Although 128 (31.4%) of the participants lacked adequate knowledge of mental illnesses, only 104 (25.6%) had relevant knowledge.154 (37.8%) respondents displayed favorable (good) attitude, whereas 82 (44.7%) displayed an unfavorable (poor) attitude toward mentally ill patients. CONCLUSION: The study revealed that nearly one-fourth of the participants appear to have adequate knowledge of mental disorders. However, 44.7% have an unfavorable attitude toward patients with mental illnesses. Hence, respondent professionals markedly correlated with both knowledge and attitude toward patients with mental illnesses, and the positive attitude strongly correlated with having adequate knowledge.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".