A 4 stage model described how clients and their relatives experienced the process of developing schizophrenia
Bibliographic record
Abstract
Barker S, Lavender T, Morant N. Client and family narratives on schizophrenia. Journal of Mental Health2001 ; 10 : 199 –212 [OpenUrl][1][CrossRef][2] QUESTION: How is the process of developing schizophrenia presented in the narratives of clients and their family members? Narrative analysis. South London. 8 clients with schizophrenia (6 men, age range 25–50 y), who were living in a hospital, hostel, or supported accommodation, and 8 close relatives (6 mothers and 2 fathers of clients). Semistructured interviews of 40–80 minutes were tape recorded, transcribed verbatim, and analysed using constant comparison. A 4 stage model was developed, which focused on events occurring before and during the first psychotic episode, at the first hospital admission, and current experiences. In narratives relating to events preceding the first psychotic episode , clients and relatives described how the onset of schizophrenia was precipitated by changes associated with adolescence, particularly difficulties in personal relationships, which disrupted a previously “normal” life. Clients' narratives about the first psychotic episode portrayed a “catastrophic disruption to their sense of self and … [1]: {openurl}?query=rft.jtitle%253DJournal%2Bof%2BMental%2BHealth%26rft.volume%253D10%26rft.spage%253D199%26rft_id%253Dinfo%253Adoi%252F10.1046%252Fj.1440-0979.2001.00211.x%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/external-ref?access_num=10.1046/j.1440-0979.2001.00211.x&link_type=DOI
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".