Leveraging crowdsourcing methods to collect qualitative data in addiction science: Narratives of non-medical prescription opioid, heroin, and fentanyl use

Strickland, Justin C.; Victor, Grant

doi:10.1016/j.drugpo.2019.10.013

Cited by 27 publications

(16 citation statements)

References 91 publications

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“…The participants were recruited through Prolific [ 44 ], a crowdsourcing platform for participant recruitment. Studies have shown that data generated via the crowdsourcing method were as reliable and valid as those obtained by offline methods, with participants reporting that they felt more comfortable sharing their personal data in crowdsourcing research than with other types of research [ 45 , 46 ]. Prolific was chosen because its participants were reported to have the most diverse demographic characteristics and to generate the highest data quality among popular crowdsourcing platforms [ 47 , 48 ].…”

Section: Methodsmentioning

confidence: 99%

Social Capital–Accrual, Escape-From-Self, and Time-Displacement Effects of Internet Use During the COVID-19 Stay-at-Home Period: Prospective, Quantitative Survey Study

Cheng¹,

Lau²,

Luk³

2020

J Med Internet Res

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Background COVID-19 has spread like wildfire across the globe, prompting many governments to impose unprecedented stay-at-home orders to limit its transmission. During an extended stay-at-home period, individuals may engage in more online leisure activities. Internet use is a double-edged sword that may have both desirable and undesirable effects on psychological well-being, and this study sought to disentangle adaptive from maladaptive internet use amidst this unusual health crisis. Objective The objectives of this study were to assess the prevalence of probable depression during the COVID-19 stay-at-home period and to test three hypothesized risk reduction or risk elevation mechanisms, namely social capital–accrual, escape-from-self, and time-displacement effects. Methods This study took place from March to May 2020 at the early stage of the pandemic. The study adopted a prospective design, with an online survey administered to 573 UK and 474 US adult residents at two assessment points 2 months apart. Results The prevalence of moderate to severe depression was 36% (bootstrap bias-corrected and accelerated [BCa] 95% CI 33%-39%) at Time 1 (ie, initial time point) and 27% (bootstrap BCa 95% CI 25%-30%) at Time 2 (ie, follow-up time point). The results supported the social capital–accrual hypothesis by showing that the approach coping style was inversely associated with Time 2 depression through its positive associations with both social networking and perceived family support. The results also supported the escape-from-self hypothesis by revealing that the avoidant coping style was positively associated with Time 2 depression through its positive associations with both gaming and cyberbullying victimization, but the serial mediation model was no longer significant after Time 1 depression and some demographic risk factors had been controlled for. Finally, the results supported the time-displacement hypothesis by showing that gaming was positively associated with Time 2 depression through its inverse associations with social networking and perceived family support. Conclusions During the extended stay-at-home period in the early stages of the COVID-19 pandemic, the prevalence of probable depression during the 2-month study period was high among the UK and US residents. Individuals with distinct coping styles may engage in different types of online leisure activities and perceive varying levels of social support, which are associated with risks of probable depression.

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Section: Methodsmentioning

confidence: 99%

Social Capital–Accrual, Escape-From-Self, and Time-Displacement Effects of Internet Use During the COVID-19 Stay-at-Home Period: Prospective, Quantitative Survey Study

Cheng¹,

Lau²,

Luk³

2020

J Med Internet Res

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“…Furthermore, the online format may prevent participants from in-depth investment in their responses, potentially prompting more shallow-level answers than we were hoping for. Recent studies have reported sufficient quality for qualitative data collected online [ 79 , 80 ], and there is an increasing number of qualitative studies using online platforms [ 81 – 85 ]. In addition, participants responses in the current study all seemed sensible to the coder.…”

Section: Discussionmentioning

confidence: 99%

People’s naïve belief about curiosity and interest: A qualitative study

et al. 2021

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The purpose of this study was to critically examine how people perceive the definitions, differences and similarities of interest and curiosity, and address the subjective boundaries between interest and curiosity. We used a qualitative research approach given the research questions and the goal to develop an in-depth understanding of people’s meaning of interest and curiosity. We used data from a sample of 126 U.S. adults (48.5% male) recruited through Amazon’s Mechanical Turk (Mage = 40.7, SDage = 11.7). Semi-structured questions were used and thematic analysis was applied. The results showed two themes relating to differences between curiosity and interest; active/stable feelings and certainty/uncertainty. Curiosity was defined as an active feeling (more specifically a first, fleeting feeling) and a child-like emotion that often involves a strong urge to think actively and differently, whereas interest was described as stable and sustainable feeling, which is characterized as involved engagement and personal preferences (e.g., hobbies). In addition, participants related curiosity to uncertainty, e.g., trying new things and risk-taking behaviour. Certainty, on the other hand, was deemed as an important component in the definition of interest, which helps individuals acquire deep knowledge. Both curiosity and interest were reported to be innate and positive feelings that support motivation and knowledge-seeking during the learning process.

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“…Crowdsourcing platforms such as Amazon Mechanical Turk (AMT) and Prolific have become widely used in psychological research over the past decade (Arditte et al, 2016;Paolacci & Chandler, 2014). Although these platforms have limitations (e.g., no possibility of biological data collection), they can be useful for quickly collecting data for research on substance use and related topics (e.g., Bounoua et al, 2021;Stanton & Watson, 2016;Strickland & Victor, 2020). Furthermore, crowdsourcing data collection methods likely will continue to play a prominent role in substance use research given the ongoing impacts of the COVID-19 pandemic, as well as the unfortunate but considerable likelihood of future pandemics (Tabish, 2020).…”

Section: Recommendations For Repeated Assessment and Psychometric Studiesmentioning

confidence: 99%

A Multisample Demonstration of Using the Prolific Platform for Repeated Assessment and Psychometric Substance Use Research

Stanton¹,

Carpenter²

2021

Preprint

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We present data from multiple samples demonstrating the Prolific platform’s potential for efficiently collecting data for substance use research involving repeated assessments, including for psychometric research requiring large samples. First, we present data from a 5-day daily diary protocol. We recruited a large sample (N = 321 at Day 1) screened for a history of self-identified mental health issues and weekly alcohol use. Participant adherence was good (82%) even without in-person contact. Alcohol use patterns conformed to theoretical expectations: Participants were more likely to drink on Fridays and Saturdays than other days, men drank more than women, and higher Alcohol Use Disorders Identification Test (AUDIT; Saunders et al., 1993) scores were associated with an increased likelihood of use and more overall drinking. Second, we present data from 429 Prolific participants screened for a history of mental health issues who completed assessments 2 weeks apart with strong retention (N = 377; 88%). We compare these data with data from undergraduates (N = 529) to demonstrate Prolific’s utility for conducting psychometrically-oriented substance use research. Internal consistency estimates for measures from the Prolific data matched or exceeded those from the undergraduate data. Furthermore, measure scores showed strong temporal stability, and factor structures (e.g., AUDIT item level structures) conformed to theoretical expectations. Collectively, findings indicate that Prolific can be used successfully for repeated measures data collection. We also provide recommendations for using Prolific to target participant recruitment for repeated measures studies, especially in the areas of psychometric and substance use research.

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Leveraging crowdsourcing methods to collect qualitative data in addiction science: Narratives of non-medical prescription opioid, heroin, and fentanyl use

Cited by 27 publications

References 91 publications

Social Capital–Accrual, Escape-From-Self, and Time-Displacement Effects of Internet Use During the COVID-19 Stay-at-Home Period: Prospective, Quantitative Survey Study

Social Capital–Accrual, Escape-From-Self, and Time-Displacement Effects of Internet Use During the COVID-19 Stay-at-Home Period: Prospective, Quantitative Survey Study

People’s naïve belief about curiosity and interest: A qualitative study

A Multisample Demonstration of Using the Prolific Platform for Repeated Assessment and Psychometric Substance Use Research

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