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Record W3035045823 · doi:10.1071/py20017

General practitioner identification and retention for people with spinal cord damage: establishing factors to inform a general practitioner satisfaction measure

2020· article· en· W3035045823 on OpenAlexaff
Ali Lakhani, David P. Watling, Ross Duncan, Peter Grimbeek, Peter Harre, Jos Stocker, Sanjoti Parekh

Bibliographic record

VenueAustralian Journal of Primary Health · 2020
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsGolder Associates (Canada)
Fundersnot available
KeywordsMedicineIdentification (biology)Exploratory factor analysisDescriptive statisticsFamily medicineClinical psychologyPsychometricsStatistics

Abstract

fetched live from OpenAlex

People with spinal cord damage (SCD) report a high level of GP use. There is a dearth of research investigating factors that contribute to GP identification and retention for people with SCD. Furthermore, a GP satisfaction measure developed specifically for people with SCD is non-existent. This preliminary study sought to identify factors contributing to GP identification and retention. A total of 266 people with SCD primarily based in Queensland, Australia, completed a cross-sectional survey that aimed to fill these knowledge gaps. Descriptive statistics and correlational analyses clarified the factors contributing to GP identification and GP retention respectively. An exploratory factor analysis utilising the principal components analysis method clarified a set of items that could underpin key domains for a SCD-specific GP satisfaction measure. The findings confirm that knowledge about SCD, physically accessible services, and trust are seminal considerations aligned with GP identification and retention for people with SCD.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.645
Threshold uncertainty score0.657

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
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 head0.111
GPT teacher head0.398
Teacher spread0.287 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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