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Record W2802339509 · doi:10.31128/ajgp-11-17-4413

How common is multiple general practice attendance in Australia?

2018· article· en· W2802339509 on OpenAlexaboutno aff
Michael Wright, Jane Hall, Kees Van Gool, Marion Haas

Bibliographic record

VenueAustralian Journal of General Practice · 2018
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsnot available
FundersAustralian Primary Health Care Research Institute, Australian National UniversityUniversity of Technology SydneyAustralian Government
KeywordsGeneral practiceAttendanceMetropolitan areaQuarter (Canadian coin)Family medicineMedicinePrimary careSample (material)Health careGeography

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: Australians can seek general practice care from multiple general practitioners (GPs) in multiple locations. This provides high levels of patient choice but may reduce continuity of care. The aim of this study was to estimate the prevalence of attendance at multiple general practices in Australia, and identify patient characteristics associated with multiple practice attendances. METHOD: A cross-sectional survey of 2477 Australian adults was conducted online in July 2013. Respondents reported whether they had attended more than one general practice in the past year, and whether they had a usual general practice and GP. Demographic information, health service use and practice characteristics were also obtained from the survey. RESULTS: Over one-quarter of the sample reported attending more than one practice in the previous year. Multiple practice attendance is less common with increasing age, and less likely for survey respondents from regional Australia, compared with respondents from metropolitan areas. Multiple practice attenders are just as likely as single practice attenders to have a usual GP. DISCUSSION: A significant proportion of general practice care is delivered away from usual practices. This may have implications for health policy, in terms of continuity and quality of primary care.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.311
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.004
Open science0.0010.000
Research integrity0.0000.003
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 head0.134
GPT teacher head0.491
Teacher spread0.357 · 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 designNot applicable
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

Citations39
Published2018
Admission routes1
Has abstractyes

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