ArticleJournal of urban health : bulletin of the New York Academy of Medicine2017
How Feasible is Multiple Time Point Web-Based Data Collection with Individuals Experiencing Street Homelessness?
Article in Journal of urban health : bulletin of the New York Academy of Medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Retaining a Sample of Homeless Youth.Journal of the Canadian Academy of Child and Adolescent Psychiatry = Journal de l'Academie canadienne de psychiatrie de l'enfant et de l'adolescent · 2018Article
- Perspectives of researchers with lived experience in implementation science research: Opportunities to close the research-to-practice gap in substance use systems of care.Implementation research and practiceArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Three barriers investigators often encounter when conducting longitudinal work with homeless or other marginalized populations are difficulty tracking participants, high rates of no-shows for follow-up interviews, and high rates of loss to follow-up. Recent research has shown that homeless populations have substantial access to information technologies, including mobile devices and computers. These technologies have the potential both to make longitudinal data collection with homeless populations easier and to minimize some of these methodological challenges. This pilot study's purpose was to test whether individuals who were homeless and sleeping on the streets-the "street homeless"-would answer questions remotely through a web-based data collection system at regular "follow-up" intervals. We attempted to simulate longitudinal data collection in a condensed time period. Participants (N = 21) completed an in-person baseline interview. Each participant was given a remotely reloadable gift card. Subsequently, weekly for 8 weeks, participants were sent an email with a link to a SurveyMonkey questionnaire. Participants were given 48 h to complete each questionnaire. Data were collected about life on the streets, service use, community inclusion, substance use, and high-risk sexual behaviors. Ten dollars was remotely loaded onto each participant's gift card when they completed the questionnaire within the completion window. A substantial number of participants (67% of the total sample and 86% of the adjusted sample) completed at least seven out of the eight follow-up questionnaires. Most questionnaires were completed at public libraries, but several were completed at other types of locations (social service agencies, places of employment, relative/friend/acquaintance's domiciles, or via mobile phone). Although some of the questions were quite sensitive, very few participants skipped any questions. The only variables associated with questionnaire completion were frequency of computer use and education-both positive associations. This pilot study suggests that collecting longitudinal data online may be feasible with a subpopulation of persons experiencing homelessness. We suspect that participant follow-up rates using web-based data collection methods have the potential to exceed follow-up rates using traditional in-person interviews. If this population of persons experiencing street homelessness can be successful with this method of data collection, perhaps other disenfranchised, difficult-to-track, or difficult-to-reach populations could be followed using web-based data collection methods. Local governments are striving to decrease the "digital divide," providing free or greatly discounted wi-fi connectivity as well as mobile computer lab access to low-income geographic areas. These actions, in combination with increased smart phone ownership, may permit vulnerable populations to connect and communicate with investigators.
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