Using Geographic Information Systems to Understand Utilization and Access to Prenatal Genetic Services (PGS).
Bibliographic record
Abstract
Settings and Life Stages: Maternal and Child Health \nUsing Geographic Information Systems to Understand Utilization and Access to Prenatal Genetic Services \nBackground \nClinical Genetic Services are changing our understanding of the epidemiology of perinatal disease. Access to genetic services can improve the health of both babies and mothers by ensuring they are receiving appropriate care during pregnancy. Providing specialist services for rare conditions in Canada is challenging due to the large geographic spread of areas. Clinical genetic services are delivered in southern Alberta, Canada using a hub and spoke model. Questions remain as to whether services are being offered to those with highest need. \nMethods \nNumerator data on utilization of genetic services (2009-2013) came from the Southern Alberta Clinical Genetics Services database, while denominator data on live births and pregnancies affected by congenital anomalies were obtained from the Alberta Perinatal Health Program and Alberta Congenital Anomalies Surveillance System. All data was provided according to 6 digit patient postal code, which allowed for geographic analysis and presentation of the data. Using a Geographic Information System, enhanced two-step floating catchment area (E2SFCA) method was used to understand spatial accessibility to genetic services. \nResults \nGeographic variability in the rate of congenital anomalies was observed across southern Alberta. The location of services did not always correspond to demand. For example, rural areas had a higher rate of anomalies and lower service utilization, while in urban areas there were lower rates of anomalies and higher rates of utilization. It was found that 10.1% of the population had no access to genetic services. In contrast, 53.3% of the population had a high access to genetic services. The E2SFCA method indicated that urban regions enjoyed greater access to genetic services while gaps in service existed in rural areas. \nConclusion \nThere are differences in utilization and spatial access to genetic services based on residential location. This information can be used to help plan appropriate locations of new services.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.010 | 0.022 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".