Does depression screening improve depression outcomes in primary care?
Bibliographic record
Abstract
Major depression is present in 5-10% of patients in primary care,1 2 including 10-20% of patients with chronic medical conditions.3 Based on the prevalence and burden of depression, the availability of screening tools, and access to potentially effective treatments, routine depression screening has been proposed as a way to improve depression care. Depression screening involves the use of self administered questionnaires or small sets of questions to identify patients who may have depression but who are not already diagnosed or being treated for depression.4 Clinical practice guidelines do not agree on whether health professionals should screen for depression in primary care. The US Preventive Services Task Force (USPSTF) recommends screening for depression when enhanced, staff assisted, depression care programmes are in place to ensure accurate diagnosis and effective treatment and follow-up.1 The Canadian Task Force on Preventive Health Care previously endorsed a similar recommendation, but in 2013 recommended against depression screening in primary care, citing a lack of evidence of benefit from randomised controlled trials and concern that a high proportion of positive screens would be false positives.5 In the UK, the National Screening Committee has determined that there is no evidence of benefit from depression screening to justify costs and potential harms and has recommended against it.6 A 2010 guideline from the National Institute for Health and Care Excellence (NICE) did not recommend routine depression screening, but suggested that clinicians be alert to possible depression, particularly among patients with a history of depression or with a chronic medical condition. NICE recommended that healthcare providers consider asking people suspected of having depression two screening questions related to depressed mood and loss of interest, and consider formal mental health assessment for people responding “yes” to either.2 In contrast to these recommendations, between 2006 and …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".