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Record W2095531748 · doi:10.1080/1360786031000072330

The use of Sections 2 and 3 of the Mental Health Act (1983) with older people: A prospective study

2003· article· en· W2095531748 on OpenAlexaff
Amy C. McPherson, RW Jones

Bibliographic record

VenueAging & Mental Health · 2003
Typearticle
Languageen
FieldPsychology
TopicHealthcare Decision-Making and Restraints
Canadian institutionsQueen's University
Fundersnot available
KeywordsScrutinyDementiaMental healthNeglectPsychiatryPsychologyMental Health ActMental illnessGerontologyMedicineDisease

Abstract

fetched live from OpenAlex

The use of the UK Mental Health Act (MHA) is under scrutiny with older people, especially in those with dementia and other organic mental disorder. Whilst research into use of the MHA with this group has been sparse, the small body of existing research suggests that the MHA is applied differently to older adults (i.e. those over 65 years). This multi-centre study identified all MHA assessments conducted over a prospective three-month period, and obtained detailed data on the circumstances behind assessment. The findings highlighted that older people assessed under the MHA tend to exhibit different behaviour patterns, circumstances and core characteristics to those under 65; older people were more likely to be detained because of self neglect and physical illness and also more often had a diagnosis of an organic mental disorder. Younger people were unlikely to have a diagnosis of organic mental disorder and were more likely to be judged as a risk to other people. Risk of suicide was particularly highlighted with the under 65 age group. Implications for legislative reform are discussed.

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)0.004
metaresearch head score (Gemma)0.011
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 score0.037
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.039
GPT teacher head0.388
Teacher spread0.348 · 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

Citations8
Published2003
Admission routes1
Has abstractyes

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