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[https://www.genesis-analytics.com/uploads//downloads/Health-2018-BMC_Public_Health.pdf] - - public:weinreich
behavior_change, HIV_AIDS, quantitative, research - 4 | id:177126 -

Typically, cascades are based on HIV treatment moni-toring data, which focus on getting people living with HIVto a point of viral suppression. HIV prevention cascadesfocus on the steps required to prevent HIV infection andsuccessfully implement HIV prevention programs. Preven-tion cascades include demand-side interventions that focuson increasing awareness, acceptability and uptake of pre-vention interventions, supply-side interventions that makeprevention interventions more accessible and available, andadherence interventions thatsupport ongoing adoption andcompliance with prevention behaviours or products...

[https://uxdesign.cc/user-research-is-more-the-merrier-9ee4cfe46c7a?ref=uxdesignweekly] - - public:weinreich
design, qualitative, quantitative, research, target_audience - 5 | id:177113 -

Small, medium or large — what sample size of users fits your study is a composite question. The magic number of 5 users may work magic in some studies while in some it may not. It depends on the constraints put on by project requirements, assumptions about problem discoverability and implications to the design process. Assess these factors to determine the number of users for your study: What’s the nature and scope of research — is it exploratory or validatory? Who and what kind of users are you planning to study? What’s the budget and time to finish the study? Does your research involve presenting statistically significant numbers or inferring behavioural estimates for the problem statement?

[https://www.qualtrics.com/events/identifying-bad-survey-respondents-attention-check-questions/?ty=mktowr-thank-you&aliId=6002] - - public:weinreich
quantitative, research - 2 | id:168142 -

Lesson: Use "commitment" question instead of attention check questions.

[https://sciencebasedmedicine.org/0-05-or-0-005-p-value-wars-continue/] - - public:weinreich
quantitative, research - 2 | id:79662 -

For fields where the threshold for defining statistical significance for new discoveries is P < 0.05, we propose a change to P < 0.005. This simple step would immediately improve the reproducibility of scientific research in many fields. Results that would currently be called “significant” but do not meet the new threshold should instead be called “suggestive.”

[https://www.ncbi.nlm.nih.gov/pubmed/26657318] - - public:weinreich
health_communication, media, research - 3 | id:76221 -

Controversy in science news accounts attracts audiences and draws attention to important science issues. But sometimes covering multiple sides of a science issue does the audience a disservice. Counterbalancing a truth claim backed by strong scientific support with a poorly backed argument can unnecessarily heighten audience perceptions of uncertainty. At the same time, journalistic norms often constrain reporters to "get both sides of the story" even when there is little debate in the scientific community about which truth claim is most valid. In this study, we look at whether highlighting the way in which experts are arrayed across truth claims-a strategy we label "weight-of-evidence reporting"-can attenuate heightened perceptions of uncertainty that can result from coverage of conflicting claims. The results of our study suggest weight-of-evidence strategies can indeed play a role in reducing some of the uncertainty audiences may perceive when encountering lop-sided truth claims.

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