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Record W2091557867 · doi:10.1177/0957154x05044604

Service utilization in 1896 and 1996: morbidity and mortality data from North Wales

2005· article· en· W2091557867 on OpenAlexaff
David Healy, Margaret Harris, Pamela Michael, Dinah Cattell, Marie Savage, Padmaja Chalasani, David Hirst

Bibliographic record

VenueHistory of Psychiatry · 2005
Typearticle
Languageen
FieldPsychology
TopicHistorical Psychiatry and Medical Practices
Canadian institutionsUniversity College of the North
Fundersnot available
KeywordsRuralityEthnic groupMental healthMedicineDemographyMental illnessService (business)PsychiatryGerontologyEnvironmental healthRural areaSociologyBusiness

Abstract

fetched live from OpenAlex

The 1896 and 1996 populations of North-West Wales are similar in number, ethnic and social mix and rurality, enabling a study of the comparative prevalence of service utilization, as well as the morbidity and mortality associated with mental illness in 1894-96 and 1996. The 1996 data reveal a 15 times greater prevalence of admissions for all diagnoses, and three times greater prevalence of admissions by detention, compared with 1896. Patients now spend more time in a service bed than they did 100 years ago. Death as a direct consequence of mental illness is commoner now than 100 years ago. There is therefore a major disjunction between the rhetoric and the reality of mental health service utilization. General factors related to changing health care and expectations and specific factors linked to the mental health appear to have led to an increased rate of service utilization in the modern period.

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)0.002
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 score0.270
Threshold uncertainty score0.536

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.161
GPT teacher head0.368
Teacher spread0.207 · 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

Citations20
Published2005
Admission routes1
Has abstractyes

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