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Record W2398240681 · doi:10.1177/229255031402200215

The unfunded costs incurred by patients accessing plastic surgical care in Northern Saskatchewan

2014· article· en· W2398240681 on OpenAlexaffabout
Jessica L Robb, Brian J Clapson

Bibliographic record

VenuePlastic Surgery · 2014
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare cost, quality, practices
Canadian institutionsUniversity of SaskatchewanUniversity of Alberta
Fundersnot available
KeywordsOperations managementMedicineBusinessEconomics

Abstract

fetched live from OpenAlex

The Canadian health care system was designed to ensure that all Canadian citizens would receive equal access to health care. However, in rural areas of Canada, patients are required to travel long distances and pay significant out-of-pocket expenses to access health care. The present study attempted to quantify the added out-of-pocket costs that rural Saskatchewan residents must pay to receive plastic surgical specialist care compared with urban residents of Saskatoon. A cost analysis was performed to generate a numerical value that would represent a minimum cost for patients travelling from three different locations within the province. The cost analysis performed in the present study approximated that the unfunded costs for common plastic surgical procedures are, at a minimum, 30 times greater for rural patients in La Ronge compared with their urban counterparts in Saskatoon. The fundamental principle of the Canadian health care system is equal access to necessary health care for all Canadians. Despite this, inequalities persist. The present cost-analysis study demonstrated that the unfunded (out-of-pocket) expenses for rural Saskatchewan patients seeking plastic surgical treatment is significantly higher than for their urban counterparts. These unfunded costs represent a significant barrier to health care access in Canada and serve to propagate inequalities in the nation's heath care system.

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)0.053
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.220
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.053
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.230
GPT teacher head0.446
Teacher spread0.216 · 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

Citations5
Published2014
Admission routes2
Has abstractyes

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