his issue of The Canadian Journal of Psychiatry includes
Bibliographic record
Abstract
an In Review focus on psychiatric epidemiology by in-vited expert contributors. It also introduces the first in a series of articles that, over several months, will discuss the results of in-depth examination of the prevalence and incidence of men-tal disorders. One might puzzle over the value of such an epi-demiological extravaganza. On the surface, psychiatric epidemiology might appear to be little more than a hapless, yawn-inducing exercise in bean-counting. Yet, a gentle scratch over the numbers, rates, and proportions exposes abundant intriguing and illuminating in-sights into many of the most important questions facing psy-chiatry. Moreover, epidemiology is the basic science of public health (1). Applied to increase understanding of Canada’s psychiatric services, it may enhance the effectiveness of pre-ventive efforts and strengthen care delivery to people affected
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 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.186 | 0.059 |
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".