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Record W3160976524 · doi:10.1071/ah20299

Developing economic measures for Aboriginal and Torres Strait Islander families on out-of-pocket healthcare expenditure

2021· article· en· W3160976524 on OpenAlexaff
Courtney Ryder, Tamara Mackean, Julieann Coombes, Kate Hunter, Shahid Ullad, Kris Rogers, Beverley M. Essue, A.J.A. Holland, Rebecca Ivers

Bibliographic record

VenueAustralian Health Review · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsCanadian Partnership Against Cancer
FundersNational Health and Medical Research CouncilMedical Research Council
KeywordsExploratory factor analysisConstruct validityHealth careMedicinePsychologyDemographyEnvironmental healthFamily medicineNursingPsychometricsPatient satisfactionClinical psychologySociologyEconomic growthEconomics

Abstract

fetched live from OpenAlex

Objective Out-of-pocket healthcare expenditure (OOPHE) has a significant impact on marginalised households. The purpose of this study was to modify a pre-existing OOPHE survey for Aboriginal and Torres Strait Islander households with children. Methods The OOPHE survey was derived through a scoping review, face and content validity, including judgement quantification with content experts. Exploratory factor analyses determined factor numbers for construct validity. Repeatability through test-retest processes and reliability was assessed through internal consistency. Results The OOPHE survey had 168 items and was piloted on 67 Aboriginal and Torres Strait Islander parents. Construct validity assessment generated a 62-item correlation matrix with a three-factor model. Across these factors, item loadings varied, 10 items with high correlations (>0.70) and 20 with low correlations (<0.40). OOPHE survey retest was conducted with 47 families, where 43 items reached slight to fair levels of agreement. Conclusion The low level of item loadings to factors in the OOPHE survey indicates interconnectedness across the three-factor model, and reliability results suggest systemic differences. Impeding factors may include cohort homogeneity and survey length. It is unknown how cultural and social nuances specific to Aboriginal and Torres Strait Islander households impacts on results. Further work is warranted. What is known about the topic? Out-of-pocket healthcare expenditure (OOPHE) are expenses not covered by universal taxpayer-funded health insurance. In elderly Australians or those with chronic conditions, OOPHE can cause substantial burden and financial hardship and, in the most extreme cases, induce bankruptcy. Despite higher hospital admissions and disease burden, little is known about how OOPHE impacts Aboriginal and Torres Strait Islander families. Additionally, in Australia, no OOPHE survey tools have been appropriately assessed; this includes for use with Aboriginal and Torres Strait Islander families. What does this paper add? This pilot study modified a pre-existing Australian OOPHE survey for use with Aboriginal and Torres Strait Islander households with children. Knowledge interface methodology was used to bring together Indigenous knowledges with quantitative survey methods. This was critical to ensuring Indigenous knowledges were central to the overall pilot study across item creation, participant focus, outcome contextualisation, interpretation, and resetting dominant norms. Outcomes have demonstrated pertinent points for future work in this area, such as the complexities in developing robust, culturally safe and specific surveys, which reach ideal psychometric levels of validity and reliability for Aboriginal and Torres Strait Islander communities. Certainly, it raises questions for current and future research using surveys in Aboriginal and Torres Strait Islander communities, which are generic and not purpose-built. What are the implications for practitioners? We recommend that OOPHE surveys should be developed with Aboriginal and Torres Strait Islander families from the outset, so they can include important contextual factors for Aboriginal and Torres Strait Islander households.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.941
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.202
GPT teacher head0.411
Teacher spread0.210 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations3
Published2021
Admission routes1
Has abstractyes

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