Healthcare Professionals' Attitudes Toward Patients With Mental Illness: A Cross-Sectional Study in Qatar
Bibliographic record
Abstract
Background: Negative attitudes toward mental illness by Health Care Professionals (HCP) have been reported in many countries across the world. Stigmatizing attitudes by HCP can have adverse consequences on people with mental illness from delays in seeking help to decreased quality of care provided. Assessing such attitudes is an essential step in understanding such stigma and, if needed, developing and testing appropriate and culturally adapted interventions to reduce it. Aims: To assess physicians and nurses attitudes toward mental illness and to determine associated factors with different levels of stigma. Methods: A cross-sectional survey was conducted among Physicians and Nurses. The Mental Illness Clinician's Attitudes (MICA) scale was used to assess attitudes toward mental illness. MICA scores range between 1 and 6 with higher values indicating higher stigmatizing attitudes. Demographic and work related information were also gathered. Descriptive statistics along with multivariate linear and multivariate logistic regression models were used. Results: A total of 406 nurses and 92 doctors participated in the study. The nurses' mean MICA score was significantly higher than that of the physicians. Among nurses, being Asian and working in a geriatric, rehabilitation and long-term care facility were associated with lower MICA scores. Among physicians, being female or graduating more than 1 year ago were also associated with lower MICA scores. Conclusion: Stigmatizing attitudes toward people with mental illness by healthcare workers are present in Qatar. They are higher among nurses as compared to physicians. Factors associated with higher stigmatizing attitudes could be used in creating appropriate intervention to reduce the magnitude of the problem.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".