MétaCan
Menu
Back to cohort
Record W3177377686 · doi:10.1016/j.pmedr.2021.101464

A cross-sectional study of financial distress in persons with multimorbidity

2021· article· en· W3177377686 on OpenAlexaboutno aff
Steven S. Coughlin, Biplab Datta, Adam E. Berman, Christos Hatzigeorgiou

Bibliographic record

VenuePreventive Medicine Reports · 2021
Typearticle
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsBehavioral Risk Factor Surveillance SystemDistressMedicinePopulationDemographyCross-sectional studyQuarter (Canadian coin)MultimorbidityFinancial distressHousehold incomeNational Health Interview SurveyGerontologyEnvironmental healthClinical psychologyGeographyEconomics

Abstract

fetched live from OpenAlex

BACKGROUND: Financial distress among persons with multimorbidity is an important topic which has been inadequately addressed to date. OBJECTIVE: We examined the extent of financial distress among persons with multimorbidity, using data from the 2017 Behavioral Risk Factor Surveillance System (BRFSS). DESIGN: Cross-sectional, population-based study. PARTICIPANTS: Adults ages ≥ 18 years with multimorbidity. MAIN MEASURES: Low income and selected social determinants of health that are indicators of financial distress. KEY RESULTS: Multimorbidity was more common among those with a household income of less than $15,000 per year (P < 0.001) and among those who were 65 years of age or older (P < 0.001). There was an approximately linear increase in the percentage of individuals who had a household income of less than $15,000 or $25,000 per year with increasing number of morbidities. About one-quarter of individuals who had five or more morbidities had a household income of less than $15,000 per year as compared with 4.49% of individuals with no morbidities (P < 0.001). For all of the social determinants of health examined (Couldn't pay bills, didn't have money for food, didn't have money for balanced meals, didn't have enough money to make ends meet, and felt this kind of stress), there was an approximately linear increase in the percentage of individuals with an indicator of financial distress with increasing number of morbidities. Further research is needed examining the prevalence and correlates of financial distress in this population as well effective strategies for ameliorating its impact on the health and wellbeing of these persons.

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)0.001
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 score0.010
Threshold uncertainty score0.557

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.046
GPT teacher head0.368
Teacher spread0.322 · 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

Citations15
Published2021
Admission routes1
Has abstractyes

Explore more

Same venuePreventive Medicine ReportsSame topicChronic Disease Management StrategiesFrench-language works237,207