Bibliographic record
Abstract
The investigators of the present study have identified Georgia as an area that could potentially have spatial equity issues with pharmacy deserts in rural areas and small cities. However, limited studies have been conducted for spatial equity of pharmacies in Georgia. Fewer studies have investigated these phenomena at the state level, and few have performed spatial correlations. Spatial equity refers to the idea that people should have equal or near-equal access to certain services (Hu et al., 2019). To fill in the gap of the literature and aid in forming solutions to pharmacy deserts in Georgia, the present study analyzed geographical patterns of spatial equity of pharmacy. Because large portions of Georgia are rural, such an analysis will elucidate how spatial equity affects such rural communities.Correlational analyses, logical regressions, and spatial analyses using Geographic Information Systems (GIS) were performed to understand the relationship between variables such as race, income level, poverty rate, and the total number of pharmacies. The study applied spatial autocorrelation and cluster analyses in ArcGIS, and statistical correlational analyses, and logical regressions in SPSS. Furthermore, the study used the 5-year (2014–2018) American Community Survey (ACS) database.At the county level, the results showed that a dense population, high median family income, and low poverty rate—among other variables—correlated with more pharmacies. In particular, total population (r(148) = .95) and population density (r(148) = .83) had a strong, positive relationship with the number of pharmacies in each county. Furthermore, the county level logical regression showed that total population and poverty rate significantly predict the number of pharmacies in each county, F(2, 146) = 648.91, P < .001, R2 = .95. At the census tract level, a low unemployment rate, high percentage of Hispanic Americans, and high median family income—among other variables—were associated with more pharmacies. In particular, total population (r(1967) = .28) had the strongest positive relationship with the number of pharmacies in each census tract. GIS global spatial autocorrelation and high-low cluster analyses further confirmed the spatial clustering of pharmacies in urban areas.Overall, results showed that counties and census tracts with lower populations and lower population densities tended to have fewer pharmacies. Because small populations and small population densities tend to couple with rurality, the results indicate that rural Georgia has less access to pharmacies than urban and suburban Georgia. In general, many pharmacy deserts exist in Georgia, and large urban areas have more access to pharmacies than rural areas and small cities. Rural areas of Georgia could experience the adverse effects of pharmacy deserts more than other non-rural areas. Overall, this analysis showed that there is a clear, positive relationship between rural counties and a lower number of pharmacies. Policy suggestions were proposed to increase access in pharmacy deserts. Keywords: spatial equity, GIS, accessibility, pharmacy desert, spatial analysis_________________ Disparites pharmaceutiques dans la Georgie rurale ResumeLes enqueteurs de la presente etude ont identifie la Georgie comme une zone qui pourrait potentiellement avoir des problemes d'equite spatiale avec les deserts pharmaceutiques dans les zones rurales et les petites villes. Cependant, des etudes limitees ont ete menees sur l'equite spatiale des pharmacies en Georgie. Peu d'etudes ont etudie ces phenomenes au niveau de l'Etat, et peu ont effectue des correlations spatiales. L'equite spatiale fait reference a l'idee que les personnes devraient avoir un acces egal ou quasi egal a certains services (Hu et al., 2019). Pour combler les lacunes de la litterature et aider a trouver des solutions aux deserts pharmaceutiques en Georgie, la presente etude a analyse les modeles geographiques d'equite spatiale de la pharmacie. Etant donne que de grandes parties de la Georgie sont rurales, une telle analyse elucidera comment l'equite spatiale affecte ces communautes rurales.Des analyses de correlation, des regressions logiques et des analyses spatiales utilisant des systemes d'information geographique (SIG) ont ete effectuees pour comprendre la relation entre des variables telles que la race, le niveau de revenu, le taux de pauvrete et le nombre total de pharmacies. L'etude a applique l'autocorrelation spatiale et les analyses typologiques dans ArcGIS, ainsi que des analyses de correlation statistique et des regressions logiques dans SPSS. De plus, l'etude a utilise la base de donnees de l'American Community Survey (ACS) sur 5 ans (2014-2018).Au niveau du comte, les resultats ont montre qu'une population dense, un revenu familial median eleve et un faible taux de pauvrete, parmi d’autres variables, etaient en correlation avec un nombre plus eleve de pharmacies. En particulier, la population totale (r(148) = .95) et la densite de population (r(148) = .83) avaient une relation forte et positive avec le nombre de pharmacies dans chaque comte. De plus, la regression logique au niveau du comte a montre que la population totale et le taux de pauvrete predisent de maniere significative le nombre de pharmacies dans chaque comte, F(2, 146) = 648,91, P < 0,001, R2 = 0,95. Au niveau du secteur de recensement, un faible taux de chomage, un pourcentage eleve d'Hispano-Americains et un revenu familial median eleve, parmi d’autres variables, etaient associes a un nombre plus eleve de pharmacies. En particulier, la population totale (r (1967) = 0,28) avait la relation positive la plus forte avec le nombre de pharmacies dans chaque secteur de recensement. L'autocorrelation spatiale globale du SIG et les analyses de classification typologique haut-bas ont confirme le regroupement spatial des pharmacies dans les zones urbaines.Dans l'ensemble, les resultats ont montre que les comtes et les secteurs de recensement ayant une population et une densite de population plus faibles avaient tendance a avoir moins de pharmacies. Etant donne que les petites populations et les faibles densites de population ont tendance a se regrouper avec la ruralite, les resultats indiquent que la Georgie rurale a moins acces aux pharmacies que la Georgie urbaine et suburbaine. En general, de nombreux deserts pharmaceutiques existent en Georgie, et les grandes zones urbaines ont plus acces aux pharmacies que les zones rurales et les petites villes. Les zones rurales de Georgie pourraient subir les effets nefastes des deserts pharmaceutiques plus que d'autres zones non rurales. Dans l'ensemble, cette analyse a montre qu'il existe une relation claire et positive entre les comtes ruraux et un nombre plus faible de pharmacies. Des suggestions de politiques ont ete proposees pour accroitre l'acces aux deserts pharmaceutiques. Mots cles: equite spatiale, SIG, accessibilite, desert pharmaceutique, analyse spatiale
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".