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Can general practice help address youth mental health? A retrospective cross‐sectional study in Dublin's south inner city

2012· article· en· W2121900125 on OpenAlexaff
Deirdre Connolly, Dorothy Leahy, Gerard Bury, Blánaid Gavin, Fiona McNicholas, David Meagher, Fergus Desmond O'Kelly, P. C. Wiehe, Walter Cullen

Bibliographic record

VenueEarly Intervention in Psychiatry · 2012
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent and Pediatric Healthcare
Canadian institutionsTrinity College
Fundersnot available
KeywordsPsychosocialGeneral practiceMedicineMental healthAnxietyCross-sectional studyEpidemiologyDepression (economics)PsychiatryIntervention (counseling)Family medicine

Abstract

fetched live from OpenAlex

AIMS: With general practice potentially having an important role in early intervention of mental and substance use disorders among young people, we aim to explore this issue by determining the prevalence of psychological problems and general practice/health service utilization among young people attending general practice. METHODS: A retrospective cross-sectional study of patients attending three general practices in Dublin city. RESULTS: Among a sample of young people (mostly women, 44% general medical services (GMS) eligible), we observed considerable contact with general practice, both lifetime and for the 2 years of the study. The mean consultation rate was 3.9 consultations in 2 years and psychosocial issues (most commonly stress/anxiety and depression) were documented in 35% of cases. Identification of psychosocial issues was associated with GMS eligibility, three or more doctor consultations, and documentation of smoking and drinking status. CONCLUSIONS: Psychosocial issues are common among young people attending general practice and more work on their epidemiology and further identification in general practice are advocated.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.084
GPT teacher head0.459
Teacher spread0.375 · 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.

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
Published2012
Admission routes1
Has abstractyes

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