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Encounters between adolescents and general practice in Australia

2008· article· en· W2164312302 on OpenAlexaff
Michael Booth, Stephanie Knox, Melissa Kang

Bibliographic record

VenueJournal of Paediatrics and Child Health · 2008
Typearticle
Languageen
FieldHealth Professions
TopicChild and Adolescent Health
Canadian institutionsCentre for Family Medicine
Fundersnot available
KeywordsMedicineGeneral practiceMental healthHealth careFamily medicineWeaknessPediatricsPsychiatrySurgery

Abstract

fetched live from OpenAlex

AIM: To describe the nature of the encounters between adolescents and general practice in Australia. METHODS: Data collected by the Bettering the Evaluation and Care of Health programme from 1998-2004 were analysed. Data for 10-14-year-old and 15-19-year-old males and females were compared with data for 25-29-year-olds. The outcome measures included: number of encounters compared with other age groups, reasons for encounter, problems managed, treatments prescribed and referrals made for key problems and types of consultations. RESULTS: Adolescents have the lowest rate of encounter with general practice, compared with all other age groups. Respiratory, skin, musculoskeletal and unspecified (fever, injury, weakness) problems accounted for the great majority of reasons for encounter and problems managed. Management of mental health problems, preventive health care and health education were very infrequently managed problems. Standard surgery consultations were more common among adolescents than among young adults. CONCLUSIONS: Adolescents have a relatively low rate of encounter with general practice and the problems managed are primarily physical ailments. There is great scope to improve delivery of preventive health care and to increase management of mental health problems.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.715

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.051
GPT teacher head0.403
Teacher spread0.352 · 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 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

Citations28
Published2008
Admission routes1
Has abstractyes

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