An App For People Of Color To Rate Their Birthing Experiences | How Different Animals See
Irth is a “Yelp-like” app to help expectant parents make informed decisions by exposing bias and racism in healthcare systems. Also, a new video camera system shows the colors of the natural world as different animals see them.
An App For People Of Color To Rate Their Birthing ExperiencesFor some patients, finding a good doctor can be as simple as looking up a doctor’s degrees and accolades. But for people who are more likely to experience discrimination in a medical setting—perhaps due to their gender, disability, sexual orientation or race—credentials only tell half the story. So how do you know where to go? And who to trust?
One app aims to help Black and brown parents-to-be make informed decisions about where they choose to give birth. Black people who give birth in the United States are far more likely than their white counterparts to experience mistreatment in hospitals, develop complications, or die due to childbirth.
Irth allows parents to leave reviews about how their birthing experience went, like: Did doctors and nurses listen to them? Was their pain taken seriously? Did they develop complications that could’ve been prevented?
Guest host Arielle Duhaime-Ross talks with Kimberly Seals Allers—journalist, activist, and founder of Irth—about why she founded the app and how it can help people.
You can learn more about Irth and download the app on their website.
Are Roses Red, And Violets Blue? Depends On Your SpeciesOver the millenia, animal eyes have evolved along different paths, adding or subtracting capabilities as they adapt to specific niches in the world. The result of all that evolution is that a bee, bird, or bull doesn’t see the world the same way you do. There are differences in the spatial resolution different animals can see, in the speed of their visual response, in the depth of focus, and in the way they process color.
Dogs, for instance, can’t really see red—their vision is best at seeing things that are blue or yellow. Birds and bees can see into the ultraviolet part of the spectrum, making a flower look quite different from the way humans perceive it.
This week, researchers published details of a video camera system that tries to help make sense of the way different animals view color. By combining different cameras, various filters, and a good dose of computer processing, they can simulate what a given video clip might look like to a specific animal species. It’s work that’s of interest to both biologists and filmmakers. Dr. Daniel Hanley, one of the researchers on the project and an assistant professor of biology at George Mason University, joins guest host Arielle Duhaime-Ross to describe the system and its capabilities.
Transcripts for each segment will be available the week after the show airs on sciencefriday.com.
Subscribe to this podcast. Follow our show on Instagram, TikTok, Facebook, and Bluesky @scifri and sign up for our newsletters. Got a science question that’s keeping you up at night? Call us: 877-472-4374
Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.
Available Results
Generated results are saved to your library for reuse and search.
Choose Template
Pick the result you want. You can review provider and model before generating.
A concise first-pass summary for understanding the episode quickly.
A comprehensive, source-grounded extraction of the reusable knowledge in an episode.
A comprehensive extraction focused on health practices, protocols, claims, and safety caveats.
A dedicated analysis of warnings, limitations, trade-offs, weak evidence, and uncertainty.
Repeatable methods, frameworks, mental models, processes, and systems.
A comprehensive extraction focused on opportunities, strategy, markets, and company building.
A concise first-pass summary for understanding the episode quickly.
A detailed readable summary organized by chapter or topic.
A navigable map of subjects, topic flow, and suggested chapters.
A comprehensive, source-grounded extraction of the reusable knowledge in an episode.
Reusable atomic knowledge units extracted from the episode.
A comprehensive extraction focused on health practices, protocols, claims, and safety caveats.
A comprehensive extraction focused on opportunities, strategy, markets, and company building.
Explicit actions, next steps, habits, recommendations, and things to avoid.
A dedicated inventory of concrete resources named in the episode.
A dedicated analysis of warnings, limitations, trade-offs, weak evidence, and uncertainty.
A concise first-pass summary for understanding the episode quickly.
A detailed readable summary organized by chapter or topic.
A navigable map of subjects, topic flow, and suggested chapters.
A comprehensive, source-grounded extraction of the reusable knowledge in an episode.
Reusable atomic knowledge units extracted from the episode.
A comprehensive extraction focused on health practices, protocols, claims, and safety caveats.
A comprehensive extraction focused on opportunities, strategy, markets, and company building.
Scientific findings, mechanisms, studies, hypotheses, and the limits of the evidence discussed.
Technologies, AI models, technical methods, capabilities, limitations, and adoption implications.
Investment theses, assets, catalysts, valuation reasoning, time horizons, and risks.
Chronologies, actors, causes, consequences, turning points, and competing historical interpretations.
Policies, proposals, stakeholders, arguments, implementation constraints, and predicted effects.
Career paths, skills, hiring signals, workplace decisions, transitions, and limitations of the advice.
Behavioral mechanisms, biases, motivation, habits, emotions, interventions, and evidence limitations.
Economic mechanisms, incentives, indicators, market structure, forecasts, and uncertainty.
Leadership principles, team systems, organizational design, culture, feedback, and failure modes.
Audience, positioning, messaging, acquisition, retention, experiments, metrics, and failed approaches.
Teaching methods, learning strategies, practice, feedback, assessment, and effectiveness evidence.
Theses, premises, arguments, objections, values, thought experiments, and unresolved questions.
Communication patterns, conflict, boundaries, expectations, repair methods, and contextual limitations.
Books, papers, authors, courses, and other learning resources mentioned in the episode.
Repeatable methods, frameworks, mental models, processes, and systems.
Explicit actions, next steps, habits, recommendations, and things to avoid.
Memorable statements and important claims with attribution and source context.
A dedicated inventory of concrete resources named in the episode.
A dedicated analysis of warnings, limitations, trade-offs, weak evidence, and uncertainty.