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Record W2007835883 · doi:10.1136/ebn.4.2.63

Properly planned de-institutionalisation for mental illness maintained most in community living with enhanced quality of life

2001· article· en· W2007835883 on OpenAlexaff
Cheryl Forchuk

Bibliographic record

VenueEvidence-Based Nursing · 2001
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsWestern UniversityLondon Health Sciences Centre
Fundersnot available
KeywordsInstitutionalisationEthnographyMedicineWeb of scienceGeneral hospitalQuality of life (healthcare)GerontologyPsychiatryInternal medicinePediatricsGeographyNursing

Abstract

fetched live from OpenAlex

Newton L, Rosen A, Tennant C , et al. Deinstitutionalisation for long-term mental illness: an ethnographic study. Aust N Z J Psychiatry2000 Jun; 34 : 484 –90 [OpenUrl][1][CrossRef][2][PubMed][3][Web of Science][4] QUESTION: What effects does de-institutionalisation have on the lives of residents who were admitted to hospital because of long term mental illness? Ethnography. Northern Sydney Area Health Service, Australia. 47 hospital residents (hospitalised from 2-43 y) transferred to the community to stay in residential facilities in middle to upper class areas. Data were collected using participant observational fieldwork, open ended and semistructured interviews, life history taking and perusal of written records. Fieldwork occurred on a daily basis over a 2.5 year period, approximately 8 months before hospital discharge and then 2 years after discharge. Data collection comprised daily field note taking, audiotaped interviews, and summaries of case records. Ethnographic themes were generated from observations occurring during the study. Initially residents had to acquire new functional living skills … [1]: {openurl}?query=rft.jtitle%253DAustralian%2Band%2BNew%2BZealand%2BJournal%2Bof%2BPsychiatry%26rft.stitle%253DAust%2BN%2BZ%2BJ%2BPsychiatry%26rft.aulast%253DNewton%26rft.auinit1%253DL.%26rft.volume%253D34%26rft.issue%253D3%26rft.spage%253D484%26rft.epage%253D490%26rft.atitle%253DDeinstitutionalisation%2Bfor%2BLong-Term%2BMental%2BIllness%253A%2BAn%2BEthnographic%2BStudy%26rft_id%253Dinfo%253Adoi%252F10.1080%252Fj.1440-1614.2000.00733.x%26rft_id%253Dinfo%253Apmid%252F10881973%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=10.1080/j.1440-1614.2000.00733.x&link_type=DOI [3]: /lookup/external-ref?access_num=10881973&link_type=MED&atom=%2Febnurs%2F4%2F2%2F63.atom [4]: /lookup/external-ref?access_num=000087555400013&link_type=ISI

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.085
GPT teacher head0.377
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2001
Admission routes1
Has abstractyes

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