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Record W2033597268 · doi:10.1353/hpu.2006.0137

Transportation Barriers to Accessing Health Care for Urban Children

2006· article· en· W2033597268 on OpenAlex
Serena Yang, Robert Zarr, Taha Kass‐Hout, Atoosa Kourosh, Nancy Kelly

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Health Care for the Poor and Underserved · 2006
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsHealth Care Foundation
Fundersnot available
KeywordsPsychological interventionLogistic regressionMedicineLow incomePrimary careEnvironmental healthBusinessNursingFamily medicineSocioeconomicsSociology

Abstract

fetched live from OpenAlex

The Texas Children's Hospital Residents' Primary Care Group Clinic provides primary care to urban low-income children. The objective of this cross-sectional study was to investigate the impact of transportation problems on a family's ability to keep an appointment. One hundred eighty-three caregivers of children with an appointment were interviewed. Caregivers who kept their appointment were compared with those who did not with respect to demographic and transportation-related characteristics. Logistic regression modeling predicted caregivers with the following characteristics were more likely not to keep an appointment: not using a car to the last kept appointment, not keeping an appointment in the past due to transportation problems, having more than two people in the household, and not keeping an appointment in the past due to reasons other than transportation problems. Future research should focus on developing interventions to help low-income urban families overcome non-financial access barriers, including transportation problems.

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.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.998

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 studies0.0030.000
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 head0.040
GPT teacher head0.401
Teacher spread0.361 · 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