Evidence-Based Mental Health
Bibliographic record
Abstract
Evidence-Based Mental HealthWith this Issue, Evidence-Based Mental Health (EBMH) celebrates 5 years of publication.This proves wrong those sceptics who thought that there was insufficient high quality evidence in the field of mental health to make a quarterly current awareness journal, such as EBMH, viable.Over time, the number of articles that meet our stringent selection criteria seems to be increasing.From the next issue (February 2003), there will be a number of changes in the way that EBMH is produced.During the last 5 years, the main editorial activity (including selection of articles, preparation of abstracts, and the coordination of the production of the journal) has taken place at the Health Information Research Unit at McMaster University in Ontario, Canada.We are enormously grateful to Brian Haynes and his team at McMaster (listed on the inside cover) for sharing their expertise and helping us to develop the journal since its inception.This editorial role will now be taken over by Bazian Ltd: a UK organ-isation, with a strong track record in the field of evidence-based health care, who already perform a similar function for other journals and evidence-based products, including Evidence-Based Cardiovascular Medicine (published by Elsevier Science) and Clinical Evidence (published by the BMJ Publishing Group).The purpose and procedures and the editors of EBMH will remain the same and we anticipate that there should be very little change to the journal itself -although we may make more additional material available on the website.We would like to take this opportunity to thank our readers and subscribers for your support over the last 5 years.As always, we are delighted to receive any feedback or suggestions on how EBMH can be improved.
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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 | 0.032 | 0.163 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".