Translational Science Education Through Citizen Science

Guided by the six elements of Translational Ecology (TE; i.e., decision-framing, collaboration, engagement, commitment, process, and communication), we showcase the first explicit example of a Translational Science Education (TSE) effort in the coastal redwood ecosystem of Humboldt County, CA. Using iNaturalist, a flexible and free citizen science/crowdsourcing app, we worked with students from grade school through college, and their teachers and community, to generate species lists for comparison among 19 school and non-profit locations spanning a range of urbanization. Importantly, this TSE effort resulted in both learning and data generation, highlighting the ability of a TSE framework to connect and benefit both students and researchers. Our data showed that, regardless of the age of the observers, holding organized BioBlitzes added substantially more species to local biodiversity lists than would have been generated without them. In support of current ecological theory, these data showed an urbanization gradient among sites, with rural sites containing fewer non-native species than urban ones. On the education side, qualitative assessments revealed students and educators remained engaged throughout the project. Future projects would also benefit by establishing quantifiable metrics for assessing student learning from project conception. Throughout the project, the fundamentals of TE were followed with repeated interactions and shared objectives developed over time within trusted community relationships. Such positive human interactions can lead new naturalists to think of themselves as champions of their local biodiversity (i.e., as land stewards). We anticipate that such newly empowered and locally expert naturalists will remain committed to land stewardship in perpetuity and that other scientists and educators are inspired to conduct similar work.

A Tool for Improving Decision Quality on Upstream Consortia

This article aims to provide a guideline to better decision quality on multi-company upstream projects. The scope is to provide a high-level overview of what should be included in a decision quality process, when companies with different levels of ownership and influence on the decision-making process naturally tend to have different approaches towards risks and decision management. It is well known in our industry that there is a predominance of multicompany participation in these projects and the paper will provide guidelines that will ultimately provide better decision quality and participant's alignment. Normally high-risk upstream projects have multi-company ownership. However, it has been noticed that companies tend to face decision management differently, which causes unnecessary delays on budget approvals and even operational timeline. Procedures to normalize the definition of an initial decision frame and creation of solutions based on a good set of alternatives are paramount to facilitate discussions and drive final recommendations. In summary, companies’ alignment on decision-making processes is key to quality, speed, and effectiveness of those decisions and critical to project success. Alignment between partners on pivotal decisions can significantly improve project delivery. The main results are practical guidelines for generating (a) decision framing, (b) strategies, (c) alternatives and potential consequences, and (d) logical analysis, partners’ alignment and commitment to action.

Bahasa IndonesiaManajemen Laba dan Perilaku Investasi: Apakah Framing Effect Diperhitungkan?

This study aims to obtain empirical evidence on the effect of earnings management on company's investment decision making, as well as to see indications of the framing effect caused by earnings management on the company's investment behavior. The sample of this study includes all manufacturing companies listed on the stock exchanges of countries in Southeast Asian region with an observation period from 2009 to 2019. The results indicate a positive and significant effect of earnings management on management investment decisions. Meanwhile, the framing effect is believed to arise as a result of earnings management practices and also influences managers in viewing further as a consideration for making company investment decisions. Keywords: Earnings Management; Investment Decision; Framing Effect.

Do versus Don’t: The Impact of Framing on Goal-Level Setting

The consumer behavior literature extensively studied the impact of goal setting on behavior and performance. However, much less is known about the antecedents of goal-level setting—consumers’ decision of whether to work out twice or three times per week. Consumers can decide how many goal-consistent activities to undertake (“goal-consistent decision frame”; such as exercising two days per week) or to forego (“goal-inconsistent decision frame”; such as not exercising five days per week). While objectively the same decision, we argue that these different frames impact consumers’ ambition. Making a decision to forego goal-consistent activities triggers negative, self-evaluative emotions and to compensate for these unfavorable self-evaluations, consumers set more ambitious goal levels. Across a variety of contexts, consumers are more ambitious when their focal decision is inconsistent with goal achievement. For instance, they decide to work out more often when they decide how many work-out sessions they would skip (vs. attend). The impact of goal-inconsistent decision framing is mitigated when the activity is less instrumental toward goal achievement, and when negative self-evaluative emotions are alleviated through self-affirmation.

Using Stakeholder Values to Promote Implementation of an Evidence-Based Mobile Health Intervention for Addiction Treatment in Primary Care Settings (Preprint)

BACKGROUND Most evidence-based practices (EBPs) do not find their way into clinical use, including evidence-based mobile health (mHealth) technologies. The literature offers implementers little practical guidance for successfully integrating mHealth into health care systems. OBJECTIVE The goal of this research was to describe a novel decision-framing model that gives implementers a method of eliciting the considerations of different stakeholder groups when they decide whether to implement an EBP. METHODS The decision-framing model can be generally applied to EBPs, but was applied in this case to an mHealth system (Seva) for patients with addiction. The model builds from key insights in behavioral economics and game theory. The model systematically identifies, using an inductive process, the perceived gains and losses of different stakeholder groups when they consider adopting a new intervention. The model was constructed retrospectively in a parent implementation research trial that introduced Seva to 268 patients in 3 US primary care clinics. Individual and group interviews were conducted to elicit stakeholder considerations from 6 clinic managers, 17 clinicians, and 6 patients who were involved in implementing Seva. Considerations were used to construct decision frames that trade off the perceived value of adopting Seva versus maintaining the status quo from each stakeholder group’s perspective. The face validity of the decision-framing model was assessed by soliciting feedback from the stakeholders whose input was used to build it. RESULTS Primary considerations related to implementing Seva were identified for each stakeholder group. Clinic managers perceived the greatest potential gain to be better care for patients and the greatest potential loss to be cost (ie, staff time, sustainability, and opportunity cost to implement Seva). All clinical staff considered time their foremost consideration—primarily in negative terms (eg, cognitive burden associated with learning a new system) but potentially in positive terms (eg, if Seva could automate functions done manually). Patients considered safety (anonymity, privacy, and coming from a trusted source) to be paramount. Though payers were not interviewed directly, clinic managers judged cost to be most important to payers—whether Seva could reduce total care costs or had reimbursement mechanisms available. This model will be tested prospectively in a forthcoming mHealth implementation trial for its ability to predict mHealth adoption. Overall, the results suggest that implementers proactively address the cost and burden of implementation and seek to promote long-term sustainability. CONCLUSIONS This paper presents a model implementers may use to elicit stakeholders’ considerations when deciding to adopt a new technology, considerations that may then be used to adapt the intervention and tailor implementation, potentially increasing the likelihood of implementation success. CLINICALTRIAL ClinicalTrials.gov NCT01963234; https://clinicaltrials.gov/ct2/show/NCT01963234 (Archived by WebCite at http://www.webcitation.org/78qXQJvVI)