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Record W4353100518 · doi:10.54691/bcpep.v8i.4307

Do People Living in Rural Areas Have Less Severe Depression Problems? Evidence from NHIS 2019 Survey Data

2023· article· en· W4353100518 on OpenAlex

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

VenueBCP Education & Psychology · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsDalhousie University
Fundersnot available
KeywordsResidenceProxy (statistics)Depression (economics)National Health Interview SurveyPropensity score matchingOrdinary least squaresRural areaSample (material)DemographyMatching (statistics)Survey data collectionGerontologyPsychologyMedicineEnvironmental healthStatisticsSociologyEconomicsPopulationMathematics

Abstract

fetched live from OpenAlex

Although many papers confirm that people living in rural areas are less likely to suffer from depression than those living in urban areas, most of them employed a straightforward linear regression, which is not convincing. Using data from the 2019 National Health Interview Survey (NHIS). This paper first identifies a proxy for depression level through Pearson's Chi-Squared test. Then, using OLS, this paper determined that place of residence had an impact on depression prevalence. To reduce the bias of the estimated the effect of place, this paper employs a propensity score matching method. Finally, the matched sample was retested to see if residence increased the risk of depression. This study suggests that there is a correlation between where you live and depression.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.123
Threshold uncertainty score0.773

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.182
GPT teacher head0.474
Teacher spread0.292 · 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