Generalised anxiety disorder detection rate in a primary care setting in Jordan: a cross-sectional study
Bibliographic record
Abstract
Background.Previous research suggests that the detection rates of Generalised Anxiety Disorders (GAD) in primary health care are low.Objectives.The aim of this study is to assess the rate of detecting Generalised Anxiety Disorder (GAD) in a family medicine clinic in Jordan and to investigate physicians' characteristics, which might predict this rate.Material and methods.This was a cross-sectional study.The sample was composed of 126 patients diagnosed as having GAD.Medical records of the patients were reviewed to determine the resident physician who provided service to each patient and whether a diagnosis of GAD was considered.15 treating physicians at different levels of vocational training were blindly included.The study explored the relationship between physicians' characteristics and the detection of GAD.Results.The total rate of recognition of GAD was 13.5%.Of the studied physicians, having taken extracurricular psychiatry courses increased the ability to diagnose GAD with an odds ratio of 3.10 and a 95% confidence interval of 1.09-8.81.Physicians in their third and fourth year of residency (seniors) were less likely to diagnose GAD than first and second year physicians (juniors), with an odds ratio of 0.28 and a confidence interval of 0.10-0.82. Conclusions.The detection rate of GAD by physicians in primary health care in Jordan is low.The importance of additional training regarding mental health issues in primary care needs to be highlighted.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".