The A-Z guide to good mental health : you don't have to be famous to have manic depression
Bibliographic record
Abstract
Get real about depression with The A-Z Guide to Good Mental Health by Jeremy Thomas and Tony Hughes. 'Everything you have always wanted to know about mental health but were afraid to ask' - Stephen Fry Are you plagued with these questions? -- HOW CAN I COPE WHEN LIFE THROWS ME OFF COURSE? HOW CAN I SEEK HELP? HOW CAN I SUPPORT A LOVED ONE? Our mental health is at times robust, at times incredibly vulnerable, and always essential to our very being. This informative and entertaining insider's guide is a rich and truthful exploration of mental health - informative but at the same time full of humour, candour and hope. The unique combination of dialogue between the authors - one a sufferer from manic depression, the other his doctor - alongside a comprehensive A-Z section, provides a fascinating insight into the subject, and contains a wealth of information on prevention, treatments, and advice on how and where to get help. Topics include: the symptoms of illness, denial, relationships, self-esteem, suicide, creativity, alcoholism and addiction; handled with warmth and humanity throughout. Ultimately, Jeremy Thomas and Tony Hughes hope that The A-Z Guide to Good Mental Health will simply help a few people in the same boat. Jeremy Thomas is a novelist and has written screenplays for television and film. He lives in West London and Greece with his wife and black Labrador, Ecco. www.jeremythomas.co.uk Dr Tony Hughes is a General Practitioner. After postgraduate hospital work he went to Australia and held a post as senior house officer in psychiatry. He also lives in West London. www.drtonyhughes.co.uk
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.024 | 0.016 |
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".