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Record W4206079424 · doi:10.1111/jrh.12643

Labor & delivery unit closures most impact travel times to birth locations for micropolitan residents in Iowa

2022· article· en· W4206079424 on OpenAlexaboutno aff
Margaret Carrel, Barbara Chebet Keino, Kelli K. Ryckman, Stephanie Radke

Bibliographic record

VenueThe Journal of Rural Health · 2022
Typearticle
Languageen
FieldMedicine
TopicMaternal and Perinatal Health Interventions
Canadian institutionsnot available
FundersMaternal and Child Health Bureau
KeywordsMetropolitan areaBirth certificateMedicaidQuarter (Canadian coin)Duration (music)SocioeconomicsGeographyDemographyMedicineUnit (ring theory)BusinessHealth careEnvironmental healthEconomic growthPsychologyPopulationSociologyEconomics

Abstract

fetched live from OpenAlex

PURPOSE: Continued closure of rural hospitals and labor & delivery units can impact timely access to care. Iowa has lost over a quarter of its labor & delivery units in the previous decade. Calculating how travel times to labor & delivery services have changed, and where in the state the largest travel times take place, are important for understanding access to this critical service. METHODS: Using parental address and facility location from birth certificate data in Iowa from 2013 to 2019, travel times to birth facility are assessed for rural, micropolitan, and metropolitan parents, as well as for complicated versus noncomplicated births and Medicaid versus non-Medicaid recipients. FINDINGS: Parts of the state have travel times that are consistently greater than 30 minutes over the duration of the study. The largest increases in travel times are found among micropolitan residents, particularly those experiencing complicated births. Travel times are consistently the longest for rural residents but increased only slightly over the study time period. CONCLUSIONS: These findings suggest that access to hospital-based obstetric care is most changed for residents of small towns rather than rural or larger city residents.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.558
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.044
GPT teacher head0.404
Teacher spread0.360 · 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

Citations6
Published2022
Admission routes1
Has abstractyes

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