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Record W4220737763 · doi:10.1002/hpm.3451

Provincial variations in not having a regular medical doctor and having unmet healthcare needs among Canadians

2022· article· en· W4220737763 on OpenAlexaffabout
Md Kamrul Islam, Peter Kellett

Bibliographic record

VenueThe International Journal of Health Planning and Management · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsHealth carePsychological interventionMedicineFamily medicineLogistic regressionCommunity healthEnvironmental healthGerontologyNursingPublic healthEconomic growth

Abstract

fetched live from OpenAlex

BACKGROUND: Despite spending 11.0% of the total gross domestic product, the quality of healthcare services in Canada has received mixed reviews. We first separately examined provincial variations in not having a regular medical doctor and having unmet healthcare needs among Canadians. Second, we evaluated provincial variations in the impact of not having a regular medical doctor on having unmet healthcare needs among Canadians. METHODS: We applied logistic regressions using data from the 2014 and 2017-2018 Canadian Community Health Surveys (CCHS). The total sample size for this study was 120,345 individuals aged 12 years and older: 61,240 from the 2014 CCHS and 59,105 from the 2017-2018 CCHS. RESULTS: We found significant provincial variations in not having a regular medical doctor and having unmet healthcare needs among Canadians. People in Quebec and the Territories were more likely not to have a regular medical doctor than their peers in Alberta. People in Quebec and the Territories were also more likely to have unmet healthcare needs than their counterparts in Alberta. Not having a regular medical doctor impacted whether Canadians reported having unmet healthcare needs to varying degrees across provinces. CONCLUSION: Findings from this study may contribute to designing province-specific policy interventions and inform efforts that seek to address barriers to having a regular medical doctor and reducing unmet healthcare needs among Canadians.

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)0.004
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 score0.294
Threshold uncertainty score0.951

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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 head0.032
GPT teacher head0.281
Teacher spread0.249 · 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

Citations7
Published2022
Admission routes2
Has abstractyes

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