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Record W2164914888 · doi:10.1108/17465729200900011

Developing an accessible and effective public mental health programme for members of the general public

2009· article· en· W2164914888 on OpenAlexaboutno aff
Lucy Tinning, Kate Harman, Rachel Lee, June S. L. Brown

Bibliographic record

VenueJournal of Public Mental Health · 2009
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthAngerAttendanceReferralAnxietyHappinessPublic healthMedicineDistressDepression (economics)PsychiatryQuarter (Canadian coin)PsychologyClinical psychologyNursingMedical educationSocial psychology

Abstract

fetched live from OpenAlex

Promoting mental health and meeting the needs of the large numbers of the general public with problems of anxiety and depression is a big challenge. Particular difficulties are the low capacity of the therapy services and the reluctance of the general public to seek help. The aim of this study was to compare the attendance, effectiveness and characteristics of participants self‐referring to six different psycho‐educational workshops, each using non‐diagnostic titles: self‐confidence; stress; sleep; relationships; happiness; and anger. The series of day‐long workshops ran for one year and were offered to members of the general public in south east London. Over a quarter had not previously sought help from their GP. The take‐up rates for the self‐confidence, sleep and anger workshops were highest and one month after attending these workshops, participants reported significantly lower depression and distress. It was concluded that a self‐referral route to some day‐long workshops can attract quite large numbers of the general public and provide access to effective psychological treatment. These workshops can be used as an effective way of promoting mental health and improving the provision of evidence‐based mental health treatment in the community, possibly within the Improving Access to Psychological Treatments (IAPT) programme in the UK.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.916
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.126
GPT teacher head0.456
Teacher spread0.330 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations2
Published2009
Admission routes1
Has abstractyes

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