A randomized double-blind trial of right prefrontal cortex low-frequency transcranial magnetic stimulation in major depression
Bibliographic record
Abstract
Methods: Data are taken from an epidemiological survey conducted with a national clustered sample of 3998 Australian adults.Following the presentation of a vignette describing depression or schizophrenia, respondents were asked a series of questions relating to their knowledge and recognition of the disorder, beliefs about the helpfulness and harmfulness of helping professionals and treatments, likely outcomes and causes, and personal and perceived stigma.Results: Participant age was coded into fi ve categories and cross-tabulated with mental health literacy variables.Multiple comparisons between the youngest age group (18-24) and all other groups showed that although young adults were better than those aged 70+ at correctly recognizing depression and schizophrenia, they were more likely to misidentify schizophrenia as depression.For those who received the depression vignette, younger adults differed from older age groups in terms of their beliefs about the helpfulness and harmfulness of certain treatments, and personal stigma.Differences were also observed between younger and older adults who received the schizophrenia vignette, specifi cally for helpfulness and harmfulness ratings, and beliefs about causes.Conclusions: Differences in mental health literacy across the adult life span suggest that more specifi c, age-appropriate messages about mental health are required to inform different age groups.The tendency for young adults to 'overidentify' depression perhaps signals the need for awareness campaigns to focus on differentiation between mental disorders.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".