Extensions of respondent-driven sampling: a new approach to the study of injection drug users aged 18–25
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There is strong evidence that opinion leader and RDS interventions are effective behavior change approaches.
“There is strong evidence that opinion leader and RDS interventions are effective behavior change approaches [20, 68].”
From Social Network Analysis for Program Implementation (2015) · cites this paper as [68]
Cites
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Self-citationTargeted Sampling: Options for the Study of Hidden Populations
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Broadhead, R. S., Heckathorn, D. D., Weakliem, D. L., Anthony, D. L., Madray, H., Mills, R., & Hughes, J. (1998) · PubMed Central · Cited by 362 · see citing works
Self-citationSome Problems of Inference from Chain Data
Erickson, B. H. (1979) · Sociological Methodology · doi:10.2307/270774 · Cited by 340 · see citing works
Sampling Rare and Elusive Populations
Sudman, S., Sirken, M. G., & Cowan, C. D. (1988) · Science · doi:10.1126/science.240.4855.991 · Cited by 237 · see citing works
Outreach-based HIV prevention for injecting drug users: a review of published outcome data.
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A generalization of the simple capture-recapture model with applications to epidemiological research
Wittes, J., & Sidel, V. W. (1968) · Journal of Chronic Diseases · doi:10.1016/0021-9681(68)90038-6 · Cited by 152 · see citing works
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Self-citationAids and Social Networks: HIV Prevention Through Network Mobilization
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Self-citationAIDS Prevention Outreach among Injection Drug Users: Agency Problems and New Approaches
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Self-citationA Methodology for Reducing Respondent Duplication and Impersonation in Samples of Hidden Populations
Heckathorn, D. D., Broadhead, R. S., & Sergeyev, B. (2001) · Journal of Drug Issues · doi:10.1177/002204260103100209 · Cited by 69 · see citing works
Self-citationSpreen, M., & Zwaagstra, R. (1994) · International Sociology · doi:10.1177/026858094009004006 · Cited by 66 · see citing works
Sampling a Hidden Population: Noninstitutionalized Drug Users
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Social Network Analysis for Program Implementation
Valente, T. W., Palinkas, L. A., Czaja, S., Chu, K.-H., & Brown, C. H. (2015) · PLOS ONE · doi:10.1371/journal.pone.0131712 · Cited by 332 · see citing works
Days With Pain and Substance Use Disorders: Is There an Association?
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Inuit, T., Smylie, J., Firestone, M., & Spiller, M. W. (2018) · Canadian Journal of Public Health · doi:10.17269/s41997-018-0111-0 · Cited by 32 · see citing works
Oliveira, L. D. P., Pereira, M. L., Azevedo, A., & Lunet, N. (2012) · Cadernos de Saúde Pública · doi:10.1590/s0102-311x2012000800010 · Cited by 18 · see citing works
- Authors
- Heckathorn, D. D., Semaan, S., Broadhead, R. S., & Hughes, J. J.
- Affiliations
- Cornell University (US)
- Centers for Disease Control and Prevention (US)
- University of Connecticut (US)
- Eastern Connecticut State University (US)
- Corresponding author
- Heckathorn, Douglas D.
- ORCID
- Hughes, James J.
- Venue
- AIDS and Behavior
- Published
- 2002-03
- Volume
- 6
- Issue
- 1
- Pages
- 55-67
- ISSN
- 1090-7165 (ISSN-L)
- 1573-3254
- OpenAlex
- W320630421
- Cited by
- 369
- References
- 17
- Citations by year
- 2026: 3 · 2025: 9 · 2024: 8 · 2023: 7 · 2022: 13 · 2021: 11 · 2020: 10 · 2019: 15 · 2018: 10 · 2017: 13 · 2016: 21 · 2015: 21 · 2014: 30 · 2013: 40 · 2012: 27
- FWCI
- 4.31
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- AIDS and Behavior (publishedVersion)
- Topics
- Health Sciences › Medicine › Epidemiology › HIV, Drug Use, Sexual Risk
- Physical Sciences › Mathematics › Statistics and Probability › Survey Sampling and Estimation Techniques
- Social Sciences › Sociology and Political Science › Survey Methodology and Nonresponse
- Keywords
- respondent-driven sampling, people who inject drugs, young adults, snowball sampling, recruitment bias, volunteering, masking, sample representativeness, personal networks, non-probability sampling
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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