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Record W3073608244 · doi:10.1017/s1744133120000250

Evolution of the determinants of unmet health care needs in a universal health care system: Canada, 2001–2014

2020· article· en· W3073608244 on OpenAlexaffabout
Ian Allan, Mehdi Ammi

Bibliographic record

VenueHealth Economics Policy and Law · 2020
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsCarleton University
Fundersnot available
KeywordsHealth careAffect (linguistics)MedicineEnvironmental healthPsychologyNursingGerontologyPolitical science

Abstract

fetched live from OpenAlex

While ensuring adequate access to care is a central concern in countries with universal health care coverage, unmet health care needs remain prevalent. However, subjective unmet health care needs (SUN) can arise from features of a health care system (system reasons) or from health care users' choices or constraints (personal reasons). Furthermore, investigating the evolution of SUN within a health care system has rarely been carried out. We investigate whether health needs, predisposing factors and enabling factors differentially affect SUN for system reasons and SUN for personal reasons, and whether these influences are stable over time, using representative data from the Canadian Community Health Surveys from 2001 to 2014. While SUN slightly decreased overall during our period of observation, the share of SUN for system reasons increased. Some key determinants appear to consistently increase SUN reporting over all our observation periods, in particular being a woman, younger, in poorer health or not having a regular doctor. The distinction between personal and system reasons is important to better understand individual experiences. Notably, women report more SUN for system reasons and less for personal reasons, and reporting system reasons increases with age. Given this stability over time, our results may inform health policymakers on which subpopulations to target to ensure access to health care is universal.

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)0.001
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.424
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.031
GPT teacher head0.363
Teacher spread0.333 · 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 designQualitative
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

Citations25
Published2020
Admission routes2
Has abstractyes

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