Mental Health Literacy of Healthcare Providers in Arab Gulf Countries: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: The concept of Mental Health Literacy (MHL) relies on our capacity to understand and recognize mental illnesses and the ability to maintain and promote a positive mentality for ourselves and others. In our review, we aim to examine the level of MHL among healthcare providers in the Arab Gulf States. METHODS: PubMed, PsycINFO, Medline were searched till August 2019. Studies were included if at least one of the main components of mental health literacy was reported, including (a) knowledge of mental illnesses, (b) stigma toward mental illnesses, (c) confidence in helping patients, and (d) behavior of helping patients, regardless of study design. The risk of bias was rated according to the modified Newcastle-Ottawa Quality Assessment Scale for cross-sectional studies. RESULTS: Seven studies were included in the review; all of them were cross-sectional, with a total of 3516 participants from the healthcare system. Overall most of the studies claimed limited knowledge, negative attitudes, behavior and/or confidence among nurses, pharmacists, and physicians, especially juniors. However, the overall quality of all outcomes was relatively very low. CONCLUSION: More high-quality evidence and in-depth qualitative studies are required to bridge the gap between mental health needs and services delivered by healthcare providers in the Gulf Arab region.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".