A Semi-Systematic Review of Patient Journey and Management of Depression in Saudi Arabia
Bibliographic record
Abstract
This semi-systematic review aimed to quantitatively map and identify data gaps in the patient journey touchpoints for depression in Kingdom of Saudi Arabia namely disease prevalence, awareness, screening, diagnosis, treatment, adherence and management. A structured search was conducted using the predefined inclusion criteria to identify relevant studies from Jan 2010–Dec 2019. To address the data gaps, an unstructured literature search and anecdotal data were also included. Data obtained were synthesized and simple or weighted mean was calculated. Of the 2,025 articles retrieved from structured and unstructured search, eight were included for final analyses. Two anecdotal data sources recommended by the local experts were also included. Most of the articles included were cross-sectional in design. The overall prevalence of depression was estimated at 18.2%. Synthesized evidence indicated that 41.8% of the patients had awareness, 44.9% received treatment and 40.7% adhered to treatment. According to anecdotal evidence, the rate of screening and diagnosis of depression was 35.0% and 55.0%, respectively, of which 60.0% of the patients achieved symptom remission. Lack of data in patient journey touchpoints for depression in Saudi Arabia highlight the need for more evidence based studies. This might improve patient care and support national level decision-making.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".