Bibliographic record
Abstract
It is time to consider alternatives to diagnosing and treating depression in primary care. GPs’ prescribing of antidepressants continues to increase, but most patients stop taking the medications soon after starting them. Depression is mostly dealt with in primary care, but GPs’ assumptions about mental health and its medical management have been inherited from the powerfully vested interests of psychiatry and pharmaceutical companies, while being influenced by financial incentives. Meanwhile, social issues continue to be overlooked in depression management guidelines, in practice, and in society at large; enabling serious engagement with psychosocial determinants of distress to be avoided. A clear-sighted revision of how best to help with patients’ emotional needs is due. The most compelling challenge to current practices of depression diagnosing and antidepressant prescribing in primary care comes from studies that show massive rates of treatment drop-out. The most recent UK research, published in the BJGP earlier this year, found that one-quarter of patients commenced on antidepressants took them for less than 30 days;1 confirming similar findings from the Netherlands.2 Other researchers have found over 50% of patients quit antidepressants before a pharmacological effect could be achieved and mostly this occurs in the absence of discussion with a GP.3,4 Some of this may be due to unwanted side-effects of the drugs, but not all, for it has been shown that a sizable proportion of patients receiving a first time prescription never even initiate drug taking.2 Nor is it due to diagnosing and prescribing that could be considered inappropriate according to existing criteria, since evidence suggests that GPs are more liable to under-diagnose5 and under-treat.6 But something in the nature of those diagnoses and treatments is evidently not right: when people present with emotional distress and we respond with symptom scores and …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| 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".