Physiognomy study that only lasts today (demographic: 20-59 year olds)

Hey there, Im a student taking part in a serious survey that will be conducted by tonight. The experiment only lasts until tonight and this page will still be up by tomorrow but no responses will be taken. I’m need a sample for how physiognomy affects dating preferences! Please respond to the right age groups and be serious about this survey as it may affect the data on a serious level and may lead to an error in the published survey. 20-39 years old male: https://forms.gle/7PQY4HFkUNiXdpFy8 40-59 years old male: https://forms.gle/QnzTPsC9B5N5RMMw6

Yale University is conducting a virtual observational study with young adults (18-25) regarding their alcohol usage. Must keep an app downloaded on your phone for a year. Earn up to $428. HIC#2000033384

Click here to apply: https://yalesurvey.ca1.qualtrics.com/jfe/form/SV\_03CshhH0p9CZ9au

Click here for more info: https://medicine.yale.edu/lab/digital/?locationId=2374

From this study:

The study confirmed earlier research that put the overall divorce or separation rate among cancer patients at 11.6 percent, similar to the population as a whole. However, researchers were surprised by the difference in separation and divorce rates by gender. The rate when the woman was the patient was 20.8 percent compared to 2.9 percent when the man was the patient.

"Female gender was the strongest predictor of separation or divorce in each of the patient groups we studied,"

https://www.sciencedaily.com/releases/2009/11/091110105401.htm

I don't see anything obviously flawed with the study. Does anybody has any further information?

I can't find any data/study bringing some light to the components of the life expectancy gap. But this should't be difficult:

• Accidents and suicide are primary cause of death until later age, these are clearly not biological factors, it should be easy to calculate life expectancy gap excluding these.
• Next, men abuse alcohol and tobacco more than women, it should be possible to compare life expectancy for non-smokers, non-drinkers only.

Please let me know if you know of any study.

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Nature: Women’s health research lacks funding – these chartsmost used indicat show how

The article is based on a flawed study by Arthur A. Mirin

Burden of Disease

How do you measure suffering caused by a disease? The technical term is called Burden of Disease and one of the two most used measures is Disability-Adjusted Life Year (DALY). The worse a disease is the more DALY it causes. And because DALY is a smart measure it understands that some diseases are worse than other and that being dead is worse that being ill. (So called Disease Weight is sometimes estimated with hypothetical trade-off between hypothetical health scenarios.)

Generally speaking, women are sick longer than men because men die while women live. That is why, men globally suffer Disease Burden of 1.35 billion DALY and women suffer Disease Burden of 1.18 billion DALY. In other words, men suffer 53.4% of all Burden of Disease.

Global Burden of Disease: https://ghdx.healthdata.org/gbd-2019

IMPORTANT NOTE: DALY is based on the ideal expected age 80 years for men and 82.5 for women. My expectation is that men should live as long as women, in which case the Disease Burden would be skewed even more towards men. But that is a different story.

Design flaws

The Arthur A. Mirin's study and the Nature article explore the ratio between the level of funding allocated for given disease by the U.S. National Institutes of Health (NIH) and the burden of that diseases, measured in DALY. They postulate theoretical "average" funding/burden ratio and categorise half of diseases (with higher ratio) as overfunded and the other half (with lover ratio) as underfunded.

In the next step, they categorise each disease as male-dominated or female-dominated and conclude that most overfunded diseases are male-dominated and most underfunded diseases are female-dominated, therefore misogyny.

This is absolute Bullshit.

Firstly, you can't simply say that Hepatitis C is a male disease because 55% of affected are males, and Breast Cancer is a female disease because 99.5% affected are women. (This way 26 disease are classified as female-dominated and only 16 as male-dominated.)

Secondly, you can't simply compare the number of male-dominated and female-dominated diseases as a measure of anything meaningful - these diseases have widely different impact. You can't say that male-dominated Substance abuse is overfunded while female-dominated Anorexia is underfunded when Substance abuse has 57-times larger Disease Burden than Anorexia. That is like comparing apples to planetoids.

Thirdly, you can't simply ignore the scientific opportunity, aka how much would given research area benefit from additional funding. Any decent study exploring the effect of any variable - like gender - on the variance in disease funding/burden ratio must acknowledge that this ratio varies hugely - by hundreds of thousands of precent - gender is a lousy predictor for this variance. For instance, compared to the "average" ratio, HIV is overfunded by factor 18 and Psoriasis is underfunded by factor of 12. HIV used to be slightly female-dominated disease until 2013 and is now considered slightly male-dominated, Psoriasis is considered gender-neutral. None of this huge difference has anything to do with gender.

The actual gender disparity in funding of diseases

The U.S. National Institutes of Health (NIH) is actually very open about what kind of research it funds. 80% of all its funding goes to projects categorised as gender neutral. Of the rest, full 70% goes to research of women’s health conditions. Male health conditions receive only 30% of gender-specific funding. Mind you, men suffer 53.4% of all Burden of Disease.

Source: NIH - Report of the Advisory Committee on Research on Women’s Health: Fiscal Years 2017–2018, page 61.

Gender Differences in Automatic In-Group Bias: Why Do Women Like Women More Than Men Like Men?

Male rights advocates cite this study en masse to prove that women implicitly demonstrate in-group bias, unlike men who show out-group (female-partial) bias. The methodology of the study included the Implicit Associations Test (IAT), which lacks construct validity and yields very weak associations, so please dispense with any studies that rely on the IAT. The study does incorporate explicit measures of in- and out-group biases, which may be subject to the social desirability effect (i.e., lying), but are infinitely better than making shit up based on associations, the meanings of which no one can actually ascertain.

Bottom line, be cautious of any conclusions drawn from the IAT.

First, I apologize for the inactivity. We're still mulling over what direction to take this sub in. So here is a poll to help us gauge what you (the previous userbase of this sub) would like to see.

Additionally, we encourage you to add your own ideas in the comments. After all, we might opt for some combination between a number of ideas.

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I recently requested moderator for this sub. Subreddit will soon be cleaned out of its previous posts and instead be used to share and discuss peer-reviewed research. Might take a while.

Edit: Cleaning has been done, sub is soon going to be used for literature reviews and/or other discussions.