Translating promise into practice: a review of machine learning in suicide research and prevention
OJ Kirtley, K van Mens, M Hoogendoorn… - The Lancet …, 2022 - thelancet.com
In ever more pressured health-care systems, technological solutions offering scalability of
care and better resource targeting are appealing. Research on machine learning as a …
care and better resource targeting are appealing. Research on machine learning as a …
Back to basics: The importance of conceptual clarification in psychological science
LF Bringmann, T Elmer… - Current Directions in …, 2022 - journals.sagepub.com
Although the lack of conceptual clarity has been observed to be a widespread and
fundamental problem in psychology, conceptual clarification plays a mostly marginal role in …
fundamental problem in psychology, conceptual clarification plays a mostly marginal role in …
Descriptive, predictive and explanatory personality research: Different goals, different approaches, but a shared need to move beyond the Big Few traits
We argue that it is useful to distinguish between three key goals of personality science—
description, prediction and explanation—and that attaining them often requires different …
description, prediction and explanation—and that attaining them often requires different …
Improving mental health services: A 50-year journey from randomized experiments to artificial intelligence and precision mental health
L Bickman - Administration and Policy in Mental Health and Mental …, 2020 - Springer
This conceptual paper describes the current state of mental health services, identifies critical
problems, and suggests how to solve them. I focus on the potential contributions of artificial …
problems, and suggests how to solve them. I focus on the potential contributions of artificial …
A conceptual framework for investigating and mitigating machine-learning measurement bias (MLMB) in psychological assessment
Given significant concerns about fairness and bias in the use of artificial intelligence (AI) and
machine learning (ML) for psychological assessment, we provide a conceptual framework …
machine learning (ML) for psychological assessment, we provide a conceptual framework …
Examining the utility of nonlinear machine learning approaches versus linear regression for predicting body image outcomes: The US Body Project I
Most body image studies assess only linear relations between predictors and outcome
variables, relying on techniques such as multiple Linear Regression. These predictor …
variables, relying on techniques such as multiple Linear Regression. These predictor …
Not just “big” data: Importance of sample size, measurement error, and uninformative predictors for developing prognostic models for digital interventions
ME McNamara, M Zisser, CG Beevers… - Behaviour research and …, 2022 - Elsevier
There is strong interest in developing a more efficient mental health care system. Digital
interventions and predictive models of treatment prognosis will likely play an important role …
interventions and predictive models of treatment prognosis will likely play an important role …
Best practices in supervised machine learning: A tutorial for psychologists
Supervised machine learning (ML) is becoming an influential analytical method in
psychology and other social sciences. However, theoretical ML concepts and predictive …
psychology and other social sciences. However, theoretical ML concepts and predictive …
Detecting careless responding in survey data using stochastic gradient boosting
U Schroeders, C Schmidt… - Educational and …, 2022 - journals.sagepub.com
Careless responding is a bias in survey responses that disregards the actual item content,
constituting a threat to the factor structure, reliability, and validity of psychological …
constituting a threat to the factor structure, reliability, and validity of psychological …