MétaCan
Menu
← Back to cohort
Record W3110567195 · doi:10.21203/rs.3.rs-112645/v1

The Rural Tax: Out-of-Pocket Costs for Patient Travel in British Columbia

2020· preprint· en· W3110567195 on OpenAlexaffabout
Jude Kornelsen, Asif Raza Khowaja, Gal Av-gay, E.E. Sullivan, Anshu Parajulee, Marjorie Dunnebacke, Dorothy F. Egan, Mickey Balas, Peggy Williamson

Bibliographic record

VenueResearch Square · 2020
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsBrock UniversityUniversity of British Columbia
Fundersnot available
KeywordsAccommodationPsychosocialRural areaHealth careBusinessMedicineEconomic growthPsychologyEconomics

Abstract

fetched live from OpenAlex

Abstract Background: A significant concern for rural patients is the cost of travel outside of their community for specialist and diagnostic care. Often, these costs are downloaded to patients and their families. Methods: Online retrospective provincial survey seeking to estimate the out-of-pocket (OOP) costs and associated experiences of rural patients traveling to access health care in British Columbia. Respondents were surveyed across five categories: Distance Traveled and Transportation Costs, Accommodation Costs, Co-Traveler Costs, Lost Wages, and Patient Stress. Results: On average, costs for respondents were $777 and $674 for transport and accommodation, respectively. Patient perspectives obtained from this survey expressed a number of related issues, including the physical and psychosocial impacts of travel as well as delayed or diminished care seeking. Conclusions: These key findings highlight the existing inequities between rural and urban patient access to health care. This study can directly inform policy related efforts towards mitigating the rural-urban gap in access to health 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.053
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.140
GPT teacher head0.374
Teacher spread0.234 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueResearch Square→Same topicHealthcare Policy and Management→French-language works237,207→