bioRxiv Science⌕ Search

bioRxiv · 10.1101/2021.10.28.466319

Assessing Motivations and Barriers to Science Outreach within Academia: A Mixed-Methods Survey

Abstract

The practice of science outreach is more necessary than ever. However, a disconnect exists between the stated goals for science outreach and its actual impact. In order to examine one potential source of this disconnect, we undertook a survey-based study to explore whether barriers to participation (either intrinsic or extrinsic) in science outreach exist within the academic community. We received responses to our survey from 530 individuals, the vast majority of whom engage in some type of science outreach activity on an annual basis. Those who engage in outreach report doing so for both personal and altruistic reasons, and having high (yet varied) levels of comfort with performing outreach activities. Respondents also report the existence of several significant yet surmountable barriers to participation, including lack of time and funding. Our findings demonstrate that both levels of participation in, and attitudes toward, science outreach within the academic community are generally favorable, suggesting that the general ineffectiveness of science outreach is due to other causes. We place our findings within the context of the broader science outreach, science communication and public engagement literature. We make recommendations on how existing approaches and infrastructure can, and must, be changed in order to improve the practice.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Woitowich, N. C., Hunt, G. C., Muhammad, L. N., Garbarino, J.. 2021-11-01. Assessing Motivations and Barriers to Science Outreach within Academia: A Mixed-Methods Survey. https://doi.org/10.1101/2021.10.28.466319

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Evaluating Large Language Models as Tools to Navigate Researchers in Rapidly Evolving Research Landscapes: A Case Study in Cancer Drug Response Prediction

Large Language Models (LLMs) have emerged as promising tools for assisting researchers in automating and accelerating the synthesis of literature reviews. However, their reliability is a significant concern due to issues like factual inaccuracies and hallucinations. The key question is whether LLMs can reliably provide comprehensive, up-to-date overviews and analyses. This study evaluates the performance of three leading LLMs (OpenAI's ChatGPT, Google's Gemini, and DeepSeek) on the complex task of generating a comprehensive survey paper on deep learning for cancer Drug Response Prediction (DRP). By testing both standard and Deep Research (DR) / Deep Think (DT) modes of LLMs with prompts of varying detail, this paper assesses key academic dimensions, including reference management, content quality, and analytical depth. Key findings reveal that while DR modes of LLMs significantly improve reliability by eliminating hallucinations, performance variations exist across models and prompts. A trade-off between reference quantity and integration quality was observed, and even the best-performing models lacked the analytical depth of human experts, often requiring extensive human supervision. The study concludes that LLMs currently serve as powerful assistive tools but still cannot replace the critical validation and synthesis provided by human researchers. Choosing the best LLM to use depends on the task in hand, while several strategies can be implemented to improve the produced output.

scientific communication and education↗

Are we moving the dial? An evaluation of sex- and gender-based analysis integration in Canadian Institutes of Health Research-funded research from 2009-2020

BackgroundSex and gender impacts health outcomes and disease risk throughout life. The health of women and members of the Two-Spirit, Lesbian, Gay, Bisexual, Transgender, Queer or Questioning, Intersex, and Asexual (2S/LGBTQ+) community is often compromised as they experience delays in diagnosis. Distinct knowledge gaps in the health of these populations has prompted funding agencies to mandate incorporation of sex and gender into research. Sex-and gender-informed research perspectives and methodology increases rigor, promotes discovery, and expands the relevance of health research. Thus, the Canadian Institutes of Health Research (CIHR) implemented a Sex and Gender-based Analysis (SGBA) framework recommending the inclusion of SGBA in project proposals in 2010 and then mandating the incorporation of SGBA into grant proposals in 2019. To examine whether this mandate resulted in increased mention of sex or gender in funded research abstracts, we searched the publicly available database of grant abstracts funded by CIHR to analyze the percentage of abstracts that mentioned sex or gender of the population to be studied. To better understand broader health equity issues we also examined whether the funded grant abstracts mentioned either female-specific health research or research within the 2S/LGBTQ+ community. ResultsWe categorized a total of 8,964 Project and Operating grant abstracts awarded from 2009- 2020 based on their study of female-specific or a 2S/LGBTQ+ populations or their mention of sex or gender. Overall, under 3% of grant abstracts funded by CIHR explicitly mentioned sex and/or gender, as 1.94% of grant abstracts mentioned sex, and 0.66% mentioned gender. As one of the goals of SGBA is to inform on health equity and understudied populations with respect to SGBA, we also found that 5.92% of grant abstracts mentioned female-specific outcomes, and 0.35% of grant abstracts focused on the 2S/LGBTQ+ community. ConclusionsAlthough there was an increased number of funded grants with abstracts that mentioned sex and 2S/LGBTQ+ health across time, these increases were less than 2% between 2009 to 2020. The percentage of funded grants with abstracts mentioning female-specific health or gender differences did not change significantly over time. The percentage of funding dollars allocated to grants in which the abstracts mentioned sex or gender also did not change substantially from 2009-2020, with grant abstracts mentioning sex or female-specific research increasing by 1.26% and 3.47% respectively, funding allocated to research mentioning gender decreasing by 0.49% and no change for 2S/LGBTQ+-specific health. Our findings suggest more work needs to be done to ensure the public can evaluate what populations will be examined with the funded research with respect to sex and gender to advance awareness and health equity in research. HighlightsO_LIThe percentage of funded grants in which the abstracts mentioned sex or gender in health research remained largely unchanged from 2009 to 2020 with the largest increase of 1.57% for those mentioning sex. C_LIO_LITotal funding amounts for grants that mentioned sex or gender in the abstract stagnated or declined from 2009 to 2020. C_LIO_LIThe percentage of funded grants in which the abstracts focusing on female-specific health did not change across 2009-2020, but the percentage of funding dollars increased by 3.47%. C_LIO_LIThe percentage of grants in which the abstracts mentioned 2S/LGBTQ+-specific health more than tripled across 2009-2020 but remained less than 1% of all funded grants. C_LI

scientific communication and education↗

A performance evaluation of neural network features and functions settings on the model accuracy

Not only in sports is a neural network the most used type of artificial intelligence. With software development, anyone can create a neural network model, but little is known about how to prepare the data and how to set up the model algorithms to their maximum performance. For these reasons, this study aims to determine whether features or function settings have a greater effect on model accuracy. An initial feature dataset (n = 18882) was obtained from publicly available sources. Each of the six different feature settings consisted of 96 models. A total of 384 models were created, in which their testing accuracy and the percentage difference between the training and testing phases were further analyzed. No statistically significant differences were found between the accuracy of the functions settings, but statistically significant differences were confirmed between the feature settings. The study found that feature settings, especially the reduction of the number of outputs, are a more important factor in increasing the model accuracy, than function settings. Although the literature focuses more on the function setting and sets feature setting is taken rather as a type of how to improve the model.

scientific communication and education↗