MétaCan
Menu
Back to cohort
Record W1976294310 · doi:10.2190/hs.44.1.b

Health Issues and Health Care Expenses in Canadian Bankruptcies and Insolvencies

2014· article· en· W1976294310 on OpenAlexfundaboutno aff
David U. Himmelstein, Steffie Woolhandler, Janis Sarra, Gordon Guyatt

Bibliographic record

VenueInternational Journal of Health Services · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsBankruptcyMedical prescriptionHealth careMedical expensesBusinessMedicineFamily medicineActuarial scienceFinanceMedical emergencyNursingEconomicsEconomic growth

Abstract

fetched live from OpenAlex

Illness can contribute to financial problems directly, through high medical bills, and indirectly, through lost income. No previous in-depth studies have documented the role of medical problems among Canadian bankruptcy filers. We obtained the bankruptcy filings from a random sample of 5,000 debtors across Canada and mailed surveys to them seeking information about the medical antecedents of their bankruptcy. A total of 521 debtors responded (response rate of 10.4%), of whom 40.1 percent reported losing at least two weeks of work-related income because of illness or injury in the two years before their filing; 8.3 percent reported a similar income loss because of caregiving responsibilities for someone else who was ill. Although 60.1 percent of respondents reported being responsible for a medical bill within the previous two years, only 6.9 percent had bills over $5,000 (all amounts in Canadian Dollars). Prescription drugs were cited as the costliest medical expense by two-thirds of debtors reporting bills > $5,000, with dental bills cited by 22.2 percent. Universal health insurance affords Canadians protection against ruinous doctor and hospital bills. Inadequate coverage for prescription drugs and dental care, however, leaves some with unaffordable out-of-pocket costs. In addition, illness is a frequent indirect cause of bankruptcy through loss of work-related income.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.665
Threshold uncertainty score0.480

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.040
GPT teacher head0.336
Teacher spread0.296 · 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

Citations9
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueInternational Journal of Health ServicesSame topicHealthcare Policy and ManagementFrench-language works237,207