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Record W2442427582 · doi:10.1177/0020731415595547

Is Team-Based Primary Care Associated with Less Access Problems and Self-Reported Unmet Need in Canada?

2015· article· en· W2442427582 on OpenAlexaffabout
Austin Zygmunt, Yukiko Asada, Fred Burge

Bibliographic record

VenueInternational Journal of Health Services · 2015
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsDalhousie University
Fundersnot available
KeywordsSocioeconomic statusEquity (law)Logistic regressionHealth careFamily medicinePrimary careMedicineNursingEnvironmental healthPopulationPolitical science

Abstract

fetched live from OpenAlex

As in many jurisdictions, the delivery of primary care in Canada is being transformed from solo practice to team-based care. In Canada, team-based primary care involves general practitioners working with nurses or other health care providers, and it is expected to improve equity in access to care. This study examined whether team-based care is associated with fewer access problems and less unmet need and whether socioeconomic gradients in access problems and unmet need are smaller in team-based care than in non-team-based care. Data came from the 2008 Canadian Survey of Experiences with Primary Health Care (sample size: 10,858). We measured primary care type as team-based or non-team-based and socioeconomic status by income and education. We created four access problem variables and four unmet need variables (overall and three specific components). For each, we ran separate logistic regression models to examine their associations with primary care type. We examined socioeconomic gradients in access problems and unmet need stratified by primary care type. Primary care type had no statistically significant, independent associations with access problems or unmet need. Among those with non-team-based care, a statistically significant education gradient for overall access problems existed, whereas among those with team-based care, no statistically significant socioeconomic gradients existed.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.177
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.068
GPT teacher head0.400
Teacher spread0.331 · 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

Citations11
Published2015
Admission routes2
Has abstractyes

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