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Record W2981885602 · doi:10.1097/won.0000000000000590

The Financial Impact of Living in Canada With an Ostomy

2019· article· en· W2981885602 on OpenAlexaffabout
Kimberly LeBlanc, Corey Heerschap, Lina Martins, Britney Butt, Samantha Wiesenfeld, Kevin Woo

Bibliographic record

VenueJournal of Wound Ostomy and Continence Nursing · 2019
Typearticle
Languageen
FieldMedicine
TopicStoma care and complications
Canadian institutionsContinental (Canada)
Fundersnot available
KeywordsGovernment (linguistics)MedicineWork (physics)BusinessHealth insuranceSocioeconomicsEnvironmental healthFinanceHealth careEconomic growthEconomics

Abstract

fetched live from OpenAlex

PURPOSE: The aim of this study was to explore how living with an ostomy financially impacts Canadians. METHODS: A descriptive, pan-Canadian, cross-sectional online and paper-based survey was conducted using a convenience sample. RESULTS: Surveys were completed by 467 individuals. Seventy-six percent (n = 355) reported spending more than $1000 annually on ostomy supplies, with 58% (n = 271) paying partially out of pocket. Atlantic regions relied primarily on insurance (n = 81), and the central, prairies, and western regions used a combination of funding (provincial government funding and/or insurance) (n = 385) with no significant out-of-pocket funding differences between regions (χ = 18.267, P = .079). Fifteen percent (n = 70) reported frequent peristomal skin problems, and 19% (n = 89) indicated that having an ostomy negatively affected their ability to work. When experiencing ostomy-related problems, 60% (n = 280) sought assistance from a nurse specialized in wound, ostomy, and continence (NSWOC) and spent significantly less on ostomy supplies (χ = 231.267, P < .001). CONCLUSION: This study demonstrated that living with an ostomy may result in financial burden and that Canadian regional variations in funding and access to an NSWOC should be explored.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.961

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.006
GPT teacher head0.261
Teacher spread0.255 · 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

Citations22
Published2019
Admission routes2
Has abstractyes

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