MétaCan
Menu
Back to cohort
Record W1996418610 · doi:10.5430/jha.v3n6p163

Gender and healthcare accessibility in Europe

2014· article· en· W1996418610 on OpenAlexvenueno aff
Maria da Conceição Constantino Portela, Adalberto Campos Fernandes

Bibliographic record

VenueJournal of Hospital Administration · 2014
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsnot available
Fundersnot available
KeywordsHealth careMedicineHealthcare systemSignificant differenceEu countriesFamily medicineDemographyGender gapEuropean unionDemographic economicsBusinessPolitical scienceSociology

Abstract

fetched live from OpenAlex

Objective: Healthcare accessibility is necessary to achieve good health outcomes. However, access to healthcare can be decreased due to distance to healthcare centres, high costs, or waiting lists. The present study explored if there is a gender gap related to healthcare accessibility constraints. Methods: We performed a cross-country transversal study to investigate the existence of gender-related healthcare accessibility constraints using Mann-Whitney U tests. The research was based on self-reported unmet needs for medical examination due to access barriers according to Eurostat. We examined annual observations from 2005 through 2011 from eight European countries: Greece, France, Germany, Ireland, Italy, Portugal, Spain, and the United Kingdom. Conclusions: We found a gender gap related to unmet medical needs due to high costs, with females more likely to have such needs. The difference in mean values related to gender was statistically significant for Greece (5.17% vs. 3.59%; p = .007), France (1.80% vs. 1.23%; p = .007), Ireland (1.53% vs. 1.13%; p = .011), and Italy (4.39% vs. 2.99%; p = .004).

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)0.001
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 score0.008
Threshold uncertainty score0.343

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 integrity0.0000.001
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 head0.053
GPT teacher head0.443
Teacher spread0.390 · 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

Citations3
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Hospital AdministrationSame topicGlobal Health Care IssuesFrench-language works237,207