Factors Associated With Calling 911 for an Overdose: An Ethnographic Decision Tree Modeling Approach

2021 ◽  
pp. e1-e3
Author(s):  
Karla D. Wagner ◽  
Brandon Koch ◽  
Jeanette M. Bowles ◽  
Silvia R. Verdugo ◽  
Robert W. Harding ◽  
...  
Keyword(s):  
Predictive Accuracy ◽  
Qualitative Interviews ◽  
Conditional Inference ◽  
People Who Use Drugs ◽  
Response Strategies ◽  
Conditional Inference Tree ◽  
Medical Response ◽  
Tree Modeling ◽  
Out Of Sample ◽  
Random Forest Models

Objectives. To identify factors that influence when people who use drugs (PWUDs) call 911 for an overdose. Methods. We conducted 45 qualitative interviews and 180 surveys with PWUDs who had recently witnessed overdoses in Southern California from 2017 to 2019. We used conditional inference tree and random forest models to generate and validate a model to predict whether 911 would be called. Results. Our model had good in- (83%) and out-of-sample (84%) predictive accuracy. Three aspects of the social and policy environment influenced calling 911 for an overdose: the effectiveness of response strategies employed, the behavior of other bystanders, and whether the responder believes it is their responsibility to call. Conclusions. Even in the presence of policies that provide some protections, PWUDs are faced with difficult decisions about calling 911 and must weigh their own safety against that of an overdose victim. Potential interventions include strengthening training and safety planning for PWUDs, bolstering protections for PWUDs when they call 911, and separating law enforcement response from emergency medical response to overdoses. (Am J Public Health. Published online ahead of print May 20, 2021: e1–e3. https://doi.org/10.2105/AJPH.2021.306261 )

Rising Stars and Underdogs: The Role Race and Parental Education Play in Predicting Mentorship

2021 ◽  
pp. 0044118X2110046
Author(s):  
Veronica Fruiht ◽  
Jordan Boeder ◽  
Thomas Chan
Keyword(s):  
Critical Race Theory ◽  
Parental Education ◽  
Individual Characteristics ◽  
Conditional Inference ◽  
Youth Of Color ◽  
Social Success ◽  
Conditional Inference Tree ◽  
Tree Models ◽  
Nationally Representative ◽  
Inference Tree

Research suggests that youth with more financial and social resources are more likely to have access to mentorship. Conversely, the rising star hypothesis posits that youth who show promise through their individual successes are more likely to be mentored. Utilizing a nationally representative sample ( N = 4,882), we tested whether demographic characteristics (e.g., race, SES) or personal resources (e.g., academic/social success) are better predictors of receiving mentorship. Regression analyses suggested that demographic, contextual, and individual characteristics all significantly predicted access to mentorship, specifically by non-familial mentors. However, conditional inference tree models that explored the interaction of mentorship predictors by race showed that individual characteristics mattered less for Black and Latino/a youth. Therefore, the rising star hypothesis may hold true for White youth, but the story of mentoring is more complicated for youth of color. Findings highlight the implications of Critical Race Theory for mentoring research and practice.

scholarly journals Women’s multiple uses of an overdose prevention technology to mitigate risks and harms within a supportive housing environment: a qualitative study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Geoff Bardwell ◽  
Taylor Fleming ◽  
Ryan McNeil ◽  
Jade Boyd
Keyword(s):  
Qualitative Interviews ◽  
Urban Setting ◽  
Supportive Housing ◽  
People Who Use Drugs ◽  
Opioid Overdose ◽  
Response Button ◽  
Gender Based Violence ◽  
Emergency Situations ◽  
Housing Environment ◽  
Gender Based

Abstract Background North America is amidst an opioid overdose epidemic. In many settings, particularly Canada, the majority of overdose deaths occur indoors and impact structurally vulnerable people who use drugs alone, making targeted housing-based interventions a priority. Mobile applications have been developed that allow individuals to solicit help to prevent overdose death. We examine the experiences of women residents utilizing an overdose response button technology within a supportive housing environment. Methods In October 2019, we conducted semi-structured qualitative interviews with 14 residents of a women-only supportive housing building in an urban setting where the overdose response button technology was installed. Data was analyzed thematically and framed by theories of structural vulnerability. Results While participants described the utility and disadvantages of the technology for overdose response, most participants, unexpectedly described alternate adoptions of the technology. Participants used the technology for other emergency situations (e.g., gender-based violence), rather than its intended purpose of overdose response. Conclusions Our findings highlight the limitations of current technologies while also demonstrating the clear need for housing-based emergency response interventions that address not just overdose risk but also gender-based violence. These need to be implemented alongside larger strategies to address structural vulnerabilities and provide greater agency to marginalized women who use drugs.

scholarly journals Predicting Mental Health From Followed Accounts on Twitter

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Cory Costello ◽  
Sanjay Srivastava ◽  
Reza Rejaie ◽  
Maureen Zalewski
Keyword(s):  
Impression Management ◽  
Online Social Networks ◽  
Ethical Issues ◽  
Predictive Accuracy ◽  
Psychological Characteristics ◽  
Post Traumatic Stress ◽  
Online Behavior ◽  
Psychological Traits ◽  
Out Of Sample ◽  
Post Traumatic

The past decade has seen rapid growth in research linking stable psychological characteristics (i.e., traits) to digital records of online behavior in Online Social Networks (OSNs) like Facebook and Twitter, which has implications for basic and applied behavioral sciences. Findings indicate that a broad range of psychological characteristics can be predicted from various behavioral residue online, including language used in posts on Facebook (Park et al., 2015) and Twitter (Reece et al., 2017), and which pages a person ‘likes’ on Facebook (e.g., Kosinski, Stillwell, & Graepel, 2013). The present study examined the extent to which the accounts a user follows on Twitter can be used to predict individual differences in self-reported anxiety, depression, post-traumatic stress, and anger. Followed accounts on Twitter offer distinct theoretical and practical advantages for researchers; they are potentially less subject to overt impression management and may better capture passive users. Using an approach designed to minimize overfitting and provide unbiased estimates of predictive accuracy, our results indicate that each of the four constructs can be predicted with modest accuracy (out-of-sample R’s of approximately .2). Exploratory analyses revealed that anger, but not the other constructs, was distinctly reflected in followed accounts, and there was some indication of bias in predictions for women (vs. men) but not for racial/ethnic minorities (vs. majorities). We discuss our results in light of theories linking psychological traits to behavior online, applications seeking to infer psychological characteristics from records of online behavior, and ethical issues such as algorithmic bias and users’ privacy.

Proactive and reactive responses to pregnancies resulting from sexual exploitation and abuse: an ecological model based on Haitian survivors’ experiences

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Luissa Vahedi ◽  
Sabine Lee ◽  
Susan A. Bartels
Keyword(s):  
Lived Experience ◽  
Child Support ◽  
Ecological Model ◽  
Qualitative Interviews ◽  
Sexual Exploitation ◽  
Third Party ◽  
Content Type ◽  
Response Strategies ◽  
Micro Level ◽  
Women And Girls

Purpose This paper aims to analyze the lived experience of seeking justice and reparations related to conceiving a peacekeeper-fathered child. Design/methodology/approach Based on 18 semi-structured qualitative interviews conducted across Haiti in 2017, the authors mapped the experiences of Haitian mothers of peacekeeper-fathered children onto the ecological framework, proposing prevention/response strategies at the micro, meso and macro levels. Findings The findings mainly focus on reporting and access to support. Reporting was sometimes discouraged by the peacekeeper fathers due to the fear of being reprimanded. Among women who did report, some were told that nothing could be done, as the peacekeeper returned to his home country. Disclosure fatigue was common among participants who formally reported their pregnancies/peacekeeper-fathered children, particularly when promises of employment or child support failed to materialize. Overall, there was widespread distrust and disillusionment with the UN’s reporting and support system. Originality/value To improve the UN’s sexual abuse and exploitation prevention/response system at the micro level, the authors propose addressing personal knowledge/attitudes/beliefs through scenario-based and contextually relevant peacekeeper training and addressing the sexual/reproductive health needs of women and girls in proximity to peacekeeping bases. At the meso level, the UN should improve trust in reporting. Efforts to do so should include mandatory third-party deoxyribonucleic acid testing and banking, streamlined reporting mechanisms and removing the practice of automatically repatriating implicated peacekeepers. At the macro level, the authors recommend investments to improve educational and economic opportunities for women and girls, as well as revamping policies that contribute to impunity and absolve peacekeepers and troop-contributing countries of their responsibilities to provide child support.

A multifactorial analysis of contact-induced change in speech reporting in written White South African English (WSAfE)

2019 ◽  
Vol 24 (1) ◽  
pp. 179-209 ◽  
Author(s):  
HAIDEE KRUGER ◽  
BERTUS VAN ROOY
Keyword(s):  
South African ◽  
Rate Of Change ◽  
Conditional Inference ◽  
Similar Frequency ◽  
Frequency Distributions ◽  
Slowing Down ◽  
Conditional Inference Tree ◽  
Contact Language ◽  
Trajectories Of Change ◽  
A Site

This article presents a corpus analysis of changes over a period of two centuries in speech-reporting constructions in written White South African English (WSAfE), a native variety of English that has been in contact with Afrikaans throughout its history. The analysis is based on register-differentiated comparable diachronic corpora of WSAfE, its parent variety, British English (BrE), and the contact language, Afrikaans. Three related reported-speech constructions are analysed, focusing on changes in the relative frequencies of variants of each construction. These constructions show ongoing change, with similar trajectories of change for WSAfE and BrE in some cases, but divergent trajectories in others. In the latter case, WSAfE and Afrikaans converge on similar frequency distributions, which follow from an accelerated rate of change or a slowing down of the rate of change for particular features in WSAfE in comparison to BrE. Descriptive findings are supported by conditional inference tree modelling. The effect of frequency on reinforcing similar patterns of change in WSAfE and Afrikaans, as well as simplification through the levelling of register differences in WSAfE and Afrikaans are proposed as explanations. The study highlights the importance of converging norms in a multilingual publication industry as a site of contact.

Bayesian hierarchical models for predicting individual performance in soccer

2018 ◽  
Vol 14 (3) ◽  
pp. 143-157
Author(s):  
Leonardo Egidi ◽  
Jonah Gabry
Keyword(s):  
Hierarchical Models ◽  
Predictive Accuracy ◽  
Individual Performance ◽  
Missing Observations ◽  
Posterior Distributions ◽  
Training Set ◽  
Bayesian Hierarchical ◽  
Posterior Predictive Checks ◽  
Modeling And Prediction ◽  
Out Of Sample

Abstract Although there is no consensus on how to measure and quantify individual performance in any sport, there has been less development in this area for soccer than for other major sports. And only once this measurement is defined, does modeling for predictive purposes make sense. We use the player ratings provided by a popular Italian fantasy soccer game as proxies for the players’ performance; we discuss the merits and flaws of a variety of hierarchical Bayesian models for predicting these ratings, comparing the models on their predictive accuracy on hold-out data. Our central goals are to explore what can be accomplished with a simple freely available dataset comprising only a few variables from the 2015–2016 season in the top Italian league, Serie A, and to focus on a small number of interesting modeling and prediction questions that arise. Among these, we highlight the importance of modeling the missing observations and we propose two models designed for this task. We validate our models through graphical posterior predictive checks and we provide out-of-sample predictions for the second half of the season, using the first half as a training set. We use Stan to sample from the posterior distributions via Markov chain Monte Carlo.

MULTIVARIATE STAR ANALYSIS OF MONEY–OUTPUT RELATIONSHIP

2001 ◽  
Vol 5 (4) ◽  
pp. 506-532 ◽  
Author(s):  
Philip Rothman ◽  
Dick van Dijk ◽  
Philip Hans
Keyword(s):  
Granger Causality ◽  
Predictive Accuracy ◽  
Information Criteria ◽  
Smooth Transition ◽  
Correction Model ◽  
Vector Error Correction ◽  
Accuracy Test ◽  
Vector Error ◽  
Out Of Sample ◽  
Smooth Transition Autoregressive

This paper investigates the potential for nonlinear Granger causality from money to output. Using a standard four-variable linear (subset) vector error-correction model (VECM), we first show that the null hypothesis of linearity can be rejected against the alternative of smooth-transition autoregressive nonlinearity. An interesting result from this stage of the analysis is that the yearly growth rate of money is identified as one of the variables that may govern the switching between regimes. Smooth-transition VECM's (STVECM's) are then used to examine whether there is nonlinear Granger causality in the money–output relationship in the sense that lagged values of money enter the model's output equation as regressors. We evaluate this type of nonlinear Granger causality with both in-sample and out-of-sample analyses. For the in-sample analysis, we compare alternative models using the Akaike information criteria, which can be interpreted as a predictive accuracy test. The results show that allowing for both nonlinearity and for money–output causality leads to considerable improvement in model's in-sample performance. By contrast, the out-of-sample forecasting results do not suggest that money is nonlinearly Granger causal for output. They also show that, according to several criteria, the linear VECM's dominate the STVECM's. However, these forecast improvements seldomly are statistically significant at conventional levels.

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