for registered nurses. Ottawa: The Association; 2002. Available: cna-aiic.ca/CNA/documents/pdf
Bibliographic record
Abstract
Editor’s note: This letter writer’s name and affiliation have been withheld at our request and with the letter writer’s consent to protect the privacy of all concerned. Treatment of mental illness in India I read with interest the article by Stephen Kisely and colleagues on in-equitable access for mentally ill pa-tients to some medically necessary pro-cedures.1 In India, the prevalence of major mental and behavioural disor-ders is estimated to be 65 per 1000 pop-ulation, which translates to 70 million patients.2,3 India’s ability to treat, care for and rehabilitate mentally ill patients leaves much to be desired. Mentally ill people are almost never taken seriously; they are treated with little or no dignity and are often locked away.4 There is only 1 trained psychiatrist for every 100 000 people with a mental illness. Most (75%) mentally ill patients live in vil-lages, where access even to basic health care is difficult. Half (53%) of the state-run psychiatric hospitals do not have a rehabilitation program. The country’s mental health budget does not exceed 1 % of total health ex-penditures. The National Mental Health Programme was implemented to provide services to rural as well as urban populations, but 80 % of people in rural areas cannot access its services. Health and labour policy-makers, in-surance companies and the general public all discriminate between physi-cal and mental health problems. Men-tally ill patients are being systematically and continuously ignored and denied the social rights they deserve.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.594 | 0.683 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".