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We lack a systematic portrait of the relationship between community involvement and how people interact with information. Young men who have sex with men (YMSM) are a population for which these relationships are especially salient: their gay community involvement varies and their information technology use is high. YMSM under age 24 are also one of the US populations with the highest risk of HIV/AIDS.
To develop, test, and refine a model of gay community involvement (GCI) factors in human-information interaction (HII) as applied to HIV/AIDS information among YMSM, specifically examining the role of Internet use in GCI and HII.
Mixed methods included: 1) online questionnaire with 194 YMSM; and 2) qualitative interviews with 19 YMSM with high GCI levels. Recruitment utilized social media, dating websites, health clinics, bars/clubs, and public postings. The survey included questions regarding HIV/AIDS–related information acquisition and use patterns, gay community involvement, risk behaviors, and technology use. For survey data, we tested multiple linear regression models using a series of community- and information-related variables as dependent variables. Independent variables included community- and information-related variables and demographic covariates. We then conducted a recursive path analysis in order to estimate a final model, which we refined through a grounded theory analysis of qualitative interview data.
Four community-related variables significantly predicted how people interact with information (HII variables): 1) gay community involvement (GCI), 2) social costs of information seeking, 3) network expertise accessibility, and 4) community relevance. GCI was associated with significantly lower perceived social costs of HIV/AIDS information seeking (
HIV/AIDS–related HII and associated technology uses are community-embedded processes. The model provides theoretical mediators that may serve as a focus for intervention: 1) valuing HIV/AIDS information, through believing it is relevant to one’s group, and 2) supportive and knowledgeable network members with whom to talk about HIV/AIDS. Pro-social community value endorsement and information sharing may also be important theoretical mediators. Our model could open possibilities for considering how informatics interventions can also be designed as community-level interventions and vice versa.
Experts increasingly recognize that human-information interaction (HII)—including acquisition, sharing, management, and use of information—is a social phenomenon. A host of research approaches have shed light on this social character, from interactionism to network analysis [
The health domain offers a critical context in which to understand the role of community involvement in HII. Disease prevalence, incidence, and outcomes may all vary at a group level. In the case of HIV/AIDS in the United States, men who have sex with men (MSM) have long had disproportionately high rates of this disease, with the rate of new infections particularly high among African-American MSM [
Due to the historical and present burden of HIV/AIDS among MSM, gay communities have mobilized an unprecedented response to the disease. Indeed, gay communities led the formation of many organizations and publications that develop and disseminate information about HIV/AIDS prevention and treatment [
H1: YMSM who are more involved in the gay community will report fewer perceived social costs of HIV/AIDS–related information seeking.
Kippax et al argued more than 20 years ago that MSM who are more involved in the gay community have more access to “informed social support” [
H2: YMSM who are more involved in the gay community will have greater network access to HIV/AIDS expertise. Those who perceive fewer social costs of seeking HIV/AIDS information will also have more of this access.
A belief that HIV/AIDS is relevant to one’s community may also be a consequence of HIV/AIDS prevention efforts and personal acquaintance with PHAs. Moreover, people may be more likely to look for information that is perceived as relevant to their community [
H3: YMSM who are more involved in the gay community will believe that HIV is more relevant to their community.
Young MSM who are more involved in the gay community may frequently encounter an HIV/AIDS information-rich environment [
H4: YMSM who are more involved in the gay community will report more incidental acquisition of HIV/AIDS information.
H5: YMSM who are more involved in the gay community will report more HIV/AIDS–related information seeking. People who perceive fewer social costs of HIV/AIDS information seeking, who see the disease as more relevant to their community, and who obtain HIV/AIDS information incidentally more often will also seek this information with more frequency.
People do not use all the information to which they have access. What factors determine information use? Certainly, information must be acquired before it is used. However, information provided by strong network ties [
H6: YMSMs’ use of HIV/AIDS information will be predicted by greater gay community involvement, higher levels of HIV/AIDS information acquisition (seeking information, incidental exposure), greater perceived relevance of HIV/AIDS to one’s community, and more network access to HIV/AIDS information (“network expertise accessibility”).
In addition to testing these hypotheses separately, we estimate a model (
H7: Community involvement will exert indirect effects on information seeking through its effect on social costs of information seeking and community relevance.
H8: Community involvement will exert indirect effects on information use through its influence on information acquisition, perceived community relevance, and network expertise accessibility.
Finally, through an inductive portion of the research, we assess the potential for new community involvement-related variables to explain the dependent variables included in the model. Therefore, we pose the following research question:
RQ1: What additional gay community-related factors, if any, may help to explain HIV/AIDS–related HII among YMSM?
Although each of the above HII processes may involve technologies, a focus on health informatics draws our attention to the extent of technological mediation of MSM’s gay community involvements and HII. According to studies, Internet use may be fundamentally changing gay communities in western countries (eg, [
H9: YMSM who are more involved in the gay community will use technologies to socialize with others more, as well as to acquire HIV/AIDS information online more than YMSM who are less involved.
Model of community involvement factors in human-information interaction.
As part of a larger investigation of HIV testing among YMSM, we conducted a parallel, mixed methods study [
We used an online questionnaire to survey a convenience sample of 194 YMSM. To engage an ethnically diverse sample, we recruited via a variety of venues, eg, social media websites, dating websites, health clinics, bars/clubs, public postings, LGBT organizations, AIDS Service Organizations (ASOs). Participants in the individual interviews were also invited to complete the online survey.
Participants completed an online self-administered survey after indicating comprehension of the informed consent material and agreement to participate in the study. The survey was pilot-tested and was administered on a dedicated website using Sawtooth software. The survey took 30-45 minutes to complete. The overall survey was distributed over 108 screens with an average of 6 questions per screen; however, skip-response patterns were used, thus reducing survey length for most participants. The survey included questions regarding HIV/AIDS–related information acquisition and use patterns, gay community involvement, risk behaviors, and technology use. Participants were also able to save in-progress surveys and return later for completion. Participants did not have the opportunity to review their responses, and there were no completeness checks, prior to submission. Each participant received a $25 e-gift card for participating.
Web survey data were collected on a secure server under 128-bit SSL encryption and a firewall. After downloading, data were expunged from the server. To prevent multiple entries [
We initially used an established, 17-item measure of gay community involvement [
To assess the possibility that YMSM had alternative community affiliations that might affect their HIV/AIDS-related HII, we asked participants to complete the following open-ended survey question, “People have different definitions for the term ‘community’. Thinking about the different communities that you belong to, please indicate below what is the community that you feel like you belong to the most.” Participants’ responses to this question were then content-analyzed by assigning emergent categories to these responses [
Based on Chatman’s theory of information poverty and its insight regarding the potential social costs of information seeking in marginalized groups [
Again, based on Chatman’s theory of information poverty [
This variable was calculated to refer to the availability of HIV/AIDS information from people close to the participant or those identified by the participant as people with whom they discussed “important personal matters,” including those with whom they have discussed or would feel comfortable discussing HIV/AIDS prevention and testing. After naming each network member, participants were asked to state whether they had ever discussed HIV/AIDS with that person and whether they considered that person “knowledgeable about HIV prevention.” Responses were on a 4-point scale (1=Completely disagree, 4=Completely agree). For each network member, an “expertise accessibility” multiplier variable was created for discussion of HIV/AIDS and the participant’s rating of that network member’s knowledge of HIV/AIDS. Then, a variable was created for “Total network expertise” accessibility, which summed the scores of expertise accessibility for all network members. Due to significant skewness, this variable was then log-transformed for statistical analyses.
This 1-item measure was adapted from the National Cancer Institute’s Health Information National Trends Survey (HINTS) [
This 4-item scale was developed based on extant theory regarding non-purposeful information acquisition, including the role of an information-rich environment in facilitating such acquisition [
We developed an original 15-item scale that assessed use of HIV/AIDS information for a variety of topics relevant to HIV/AIDS risk and prevention. Principal axis factor analyses with varimax rotation showed that a single factor with an Eigenvalue of 8.962 explained 59.744% of the variance. Thus, a 10-item scale was created with responses to the question “In which of the following ways did you use the HIV/AIDS information that you got in the past 12 months? Did you use the information to...”. Options included finding a place to get tested for HIV, deciding whether to ask a partner his HIV status, deciding whether to get tested for HIV, and deciding whether to ask a partner to obtain an HIV test. Responses were on a 5-point scale (1=Never, 5=A great deal). The scale had excellent reliability (Cronbach alpha=.937).
Participants were asked whether or not they have technologies that may provide Internet access, including desktop/laptop computers, cell phone/smartphone, PDA, e-readers, music players, and game consoles.
Participants were asked how often they use the Internet at a variety of locations. Options included home, school, work, public library or community center, mobile device, or other. The response scale varied from “Less often than every few weeks or never” to “Several times a day”. Due to high levels of Internet use in the sample, binary variables were then created across all Internet access locations to note whether the participant “Uses the Internet several times a day” or “Uses the Internet less than several times a day”.
As mentioned, participants were asked to specify up to 7 people with whom they discussed “important personal matters”. Each participant was asked how often they communicate weekly with each of these named network members using the Internet, phone (not including texting), or face to face. They were also asked how many texts they sent per day to that person. Daily texts were then transformed into a weekly value. Following this, the proportion of overall daily contacts with each network member through each communication medium was calculated. This number was then used to calculate an overall average for each communication media for each participant across all of their network members.
Participants were asked how they met each of their network members. Response options included family, school, social gathering/through friends, online, work, and other. A binary variable was created to indicate whether a network member was met online. The total number of network members whom the participant had met online was then calculated. Because this was a highly skewed variable, this number was transformed into a binary variable for each participant for whether or not he had met any network members online.
Participants were asked how many times in the past 2 months they had used the Internet to: 1) find someone to date, or 2) to “hook up” (ie, have a sexual encounter). The 7-point response scale ranged from “Never” to “More than once a day”. Because these variables were skewed, a binary variable was created to reflect whether or not the person had used the Internet for either purpose in the past 2 months.
Participants were asked how much time they spend hanging out with other MSM by “chatting on the Internet”. The 4-point response scale ranged from 1=Not at all to 4=More than 10 hours.
Participants were asked how much they had used three online source types to obtain HIV/AIDS information in the past 12 months. Options included “Internet sites for men who have sex with men”, “Social networking sites (like Facebook or Twitter)”, and “All other Internet sites”. The 4-point response scale ranged from 1=Never to 4=Often. A principal axis factor analysis with varimax rotation was conducted, producing one factor with an Eigenvalue of 2.52 that explained 75.075% of the variance. Values on this new scale were skewed, and therefore, were classified as never, often, or rarely/occasionally using any online HIV/AIDS information source.
Participants were asked to state their age, race (White/European American, Black/African American, Asian, Native American/Alaska Native, Hawaiian/Pacific Islander and Other), ethnicity (Hispanic/Latino or not), sexual identity (gay/bisexual/heterosexual), and highest level of education completed. Due to the disparity between whites and African-American and Latino MSM in new HIV infections, a binary “minority” variable was created for African Americans and Latinos. Due to the distribution of the education variables, we also created a binary education variable to indicate whether the participant had a high school education or an education beyond high school.
We calculated descriptive statistics about the respondents’ gay community involvement, categories for the community to which they most belong, HII, technology use, and demographics. We then tested multiple linear regression models that took each of the key community- and information-related variables as the dependent variables. The independent variables in these models included community- and information-related variables, as well as demographic covariates. Assumptions for multiple linear regressions were met. Skewness and kurtosis values for the dependent and independent variables were within range for normality, and residuals plots and partial plots looked acceptable. Lack of multicollinearity among the predictors was indicated by all Pearson’s correlation measures being < 0.7, variance inflation factor values < 10, and tolerance values > 0.10. Cook’s
Due to the modest levels of variance in HII predicted by our regression models (
In-depth, semi-structured interviews [
We conducted a grounded theory [
Survey participants’ average age was 20.66 (see
The majority felt that the community to which they most belonged was the Gay/Queer/LGBT community (65.8%), with the next most common response being none (10.2%). Several YMSM defined their primary community as smaller subgroups of people united around alternative principles, such as shared values (3.7%) or friendship/kinship (4.8%). However, it is likely that these groups included other MSM, since 4 (25%) of the participants who chose these smaller subgroups also indicated that “some” or “all” of their friends were MSM, and 8 (50%) stated that “a few” were. A minority of participants (15.5%) “most belonged to” an alternative social group. The most frequently named alternative social groups were school/workplace (7.0%), city/neighborhood (2.1%), style/fashion subculture (2.1%), sports/recreation (1.6%), ethnic/cultural group (1.6%), and churches (1.1%). There was a large association [
As might be expected with a web survey sample, participants were heavy Internet users, with 89.7% of respondents using the Internet several times a day (see
Like the survey participants, the mean interview participant age was just under 21, and the majority was African American and gay-identified (see
In support of Hypothesis 2, gay community involvement and social costs had significant associations with access to HIV/AIDS expertise in personal networks. On an unadjusted basis, participants with more education and those who were racial/ethnic minorities had less access to HIV/AIDS expertise in their networks, but these effects disappeared after adjustment for community involvement and social costs. 14% of the overall variance in network expertise was accounted for in the final model.
Community relevance was predicted on an unadjusted basis by community involvement, social costs, and network expertise access, although it was not predicated on any demographic covariates. However, the final model, which accounted for 14% of the variance in community relevance, included only community involvement as a significant predictor. Thus, Hypothesis 3 was supported.
Hypothesis 5 also received support. Those with greater gay community involvement had sought HIV/AIDS information more frequently than those with less involvement. Social costs of information seeking, community relevance, and IIA were all significant predictors of information seeking frequency on an unadjusted basis. However, each of these effects disappeared in the full regression model, leaving only community involvement as a significant gay community-related predictor. This result meant that Hypothesis 7 was unsupported, since social costs and community relevance could not act as mediators between community involvement and information seeking without these variables having a direct association with information seeking. As for covariates, minority men sought HIV/AIDS information more frequently than whites; this variable was significant in the final model, although its contribution to prediction was smaller than community involvement (
The most robust regression model sought to predict HIV/AIDS information use, with 28% of the variance in the model explained by included variables: community relevance, network expertise access, and both IIA and information seeking. Therefore, Hypothesis 6 was supported. The magnitude of effect for community relevance (path coefficient=.273) was comparable to that for incidental information acquisition and seeking (path coefficients=.215 and .284, respectively). Significant direct effects for information use disappeared once adjusted for its mediators. Thus, all effects for community involvement on information use were indirect, providing support for Hypothesis 8 (see
Therefore, overall, four community-related variables were significant in predicting the amount of information acquisition and/or use: 1) community involvement, 2) social costs of information seeking, 3) network expertise accessibility, and 4) community relevance. The final path model predicted 28% of the variance in information use, 14% of the variance in incidental information acquisition, and 9% of the variance in information seeking (see
A recursive path analysis with observed variables was estimated with AMOS structural equation modeling software version 19. The resulting model is depicted in
Hypothesis 9 received partial support. Significant positive relationships exist between gay community involvement and use of the Internet at least several times a day (
Due to the modest predictive power of the existing model for HII-related dependent variables, we sought to refine our model by investigating what additional gay community-related factors, if any, may help to explain HIV/AIDS–related HII among YMSM (RQ1). Our grounded theory analysis of interview transcripts yielded a key category:
A consequence of information sharing was
Survey participant demographics (n=194).
|
|
Number | Valid Percent |
|
|
20.66 (1.71) |
|
|
|
|
|
|
Black/African American | 111 | 57.2 |
|
White/European American | 75 | 38.7 |
|
Native American/Native Hawaiian/Pacific Islander | 10 | 5.2 |
|
Asian | 12 | 6.2 |
|
Other | 13 | 6.7 |
|
|
34 | 17.5 |
|
|
|
|
|
Some high school | 10 | 5.2 |
|
High school/GED | 92 | 47.4 |
|
Technical school | 3 | 1.5 |
|
Some college | 69 | 35.6 |
|
Bachelor’s/graduate degree | 18 | 9.8 |
|
|
|
|
|
Gay | 154 | 84.5 |
|
Bisexual | 26 | 13.5 |
|
Heterosexual | 5 | 3.6 |
|
Other | 6 | 3.1 |
|
|
15 | 11.6 |
|
|
|
|
|
Gay/Queer/LGBT | 123 | 65.8 |
|
None | 19 | 10.2 |
|
School/Workplace | 13 | 7.0 |
|
Family/friends | 9 | 4.8 |
|
Values-based community (eg, communication, love, togetherness, beauty) | 7 | 3.7 |
|
City/neighborhood | 4 | 2.1 |
|
Style/fashion (eg, urban prep, stoner) | 4 | 2.1 |
|
Sports/recreation | 3 | 1.6 |
|
Ethnic/cultural group | 3 | 1.6 |
|
Church | 2 | 1.1 |
a More than one response possible.
Survey participants’ technology use and information interaction (n=194).
|
Number | Valid Percent | |
|
|
|
|
|
Desktop computer | 71 | 36.8 |
|
Laptop computer | 118 | 61.5 |
|
Cell phone (including smart phones such as iPhone, Android, BlackBerry or similar device) | 153 | 80.5 |
|
PDA or personal data device | 17 | 8.9 |
|
E-reader (eg, Kindle, iPad) | 36 | 18.6 |
|
iPod or MP3 player | 126 | 65.6 |
|
Game console (eg, Xbox, Playstation) | 100 | 53.2 |
|
|
|
|
|
Several times a day | 174 | 89.7 |
|
At least once a day | 18 | 9.3 |
|
Less than once a day | 2 | 1 |
|
|
|
|
|
Mean proportion on Internet (SD) | 0.12 (0.13) |
|
|
Mean proportion on texting (SD) | 0.43 (0.19) |
|
Mean proportion on phone (not including texting) (SD) | 0.12 (0.11) |
|
|
|
Mean proportion on face-to-face (SD) | 0.19 (0.15) |
|
|
|
24 | 12.4 |
|
|
70 | 36.5 |
|
|
|
|
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More than 10 hours | 27 | 14.1 |
|
3-10 hours | 37 | 19.4 |
|
Up to 3 hours | 47 | 24.6 |
|
Not at all | 80 | 41.9 |
|
|
|
|
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Very often | 27 | 13.9 |
|
Often | 31 | 16.0 |
|
Sometimes | 72 | 37.1 |
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Rarely | 37 | 19.1 |
|
Never | 27 | 13.9 |
|
|
|
|
|
Often | 15 | 7.7 |
|
Occasionally or rarely | 99 | 59.6 |
|
Never | 52 | 31.3 |
Interview participant demographics (n=19).
|
Number | Valid Percent | |
|
20.79 (1.96) |
|
|
|
|||
|
Black/African American | 12 | 63.2 |
|
White/European American | 4 | 21.1 |
|
Native American/Native Hawaiian/Pacific Islander | 2 | 10.5 |
|
Asian | 1 | 5.3 |
|
3 | 15.8 | |
|
|||
|
Gay | 13 | 68.4 |
|
Bisexual | 6 | 31.6 |
|
1 | 5.3 |
a More than one response possible.
Linear regressions for community-related variables.
Independent variable | Dependent variable | ||||||||
|
|
Gay community involvement (GCI) | Social costs of HIV/AIDS information seeking | Network access to HIV/AIDS expertise | Community relevance of HIV/AIDS information | ||||
|
|
Beta |
|
Beta |
|
Beta |
|
Beta |
|
Age | Unadj. | –.070 | .346 | –.103 | .154 | .079 | .272 | .011 | .881 |
|
Adj. | — | — | — | — | — | — | — | — |
Education level | Unadj. | .001 | .988 | .163 | .023 | –.269 | <.001 | –.083 | .256 |
|
Adj. | — | — | .164 | .022 | –.057 | .422 | — | — |
Racial /ethnic minority | Unadj. | .064 | .393 | –.075 | .296 | –.173 | .016 | –.003 | .968 |
|
Adj. | — | — | — | — | –.031 | .654 | — | — |
Gay community involvement (GCI) | Unadj. | — | — | –.272 | <.001 | .271 | <.001 | .356 | <.001 |
|
Adj. | — | — | –.272 | <.001 | .318 | <.001 | .303 | <.001 |
Social costs of HIV/AIDS information seeking | Unadj. | — | — | — | — | –.345 | <.001 | –.242 | .001 |
|
Adj. | — | — | — | — | –.149 | .044 | –.142 | .062 |
Network access to HIV/AIDS expertise | Unadj. | — | — | — | — | — | — | .184 | .011 |
|
Adj. | — | — | — | — | — | — | .053 | .485 |
Community relevance of HIV/AIDS information | Unadj. |
|
|
|
|
|
|
|
|
|
Adj. |
|
|
|
|
|
|
|
|
Incidental HIV/AIDS information acquisition frequency | Unadj. | — | — | — | — | — | — | — | — |
|
Adj. | — | — | — | — | — | — | — | — |
Frequency of HIV/AIDS information seeking | Unadj. | — | — | — | — | — | — | — | — |
|
Adj. | — | — | — | — | — | — | — | — |
R2 adjusted |
|
— |
|
.091 |
|
.135 |
|
.139 |
|
Linear regressions for information interaction variables.
Independent variable | Dependent variable | ||||||
|
|
Incidental HIV/AIDS information acquisition frequency | Frequency of HIV/AIDS information seeking | HIV/AIDS information use for decision making | |||
|
|
Beta |
|
Beta |
|
Beta |
|
Age | Unadj. | –.130 | .070 | .065 | .368 | –.026 | .724 |
|
Adj. | –.031 | .686 | — | — | — | — |
Education level | Unadj. | .180 | .012 | .001 | .984 | .008 | .913 |
|
Adj. | .168 | .030 | – | – | – | – |
Racial /ethnic minority | Unadj. | –.030 | .675 | .234 | .001 | .092 | .200 |
|
Adj. | — | — | .226 | .001 | — | — |
Gay community involvement (GCI) | Unadj. | .378 | <.001 | .301 | <.001 | .257 | <.001 |
|
Adj. | .368 | <.001 | .192 | .020 | –.059 | .448 |
Social costs of HIV/AIDS information seeking | Unadj. | .012 | .868 | –.194 | .007 | –.108 | .135 |
|
Adj. | — | — | –.100 | .175 | — | — |
Network access to HIV/AIDS expertise | Unadj. | –.014 | .850 | .071 | .324 | .244 | .001 |
|
Adj. | — | — | — | — | .192 | .005 |
Community relevance of HIV/AIDS information | Unadj. | .137 | .058 | .206 | .004 | .375 | <.001 |
|
Adj. | .020 | .784 | .103 | .173 | .273 | <.001 |
Incidental HIV/AIDS information acquisition frequency | Unadj. | — | — | .162 | .024 | .274 | <.001 |
|
Adj. | — | — | .083 | .274 | .215 | .002 |
Frequency of HIV/AIDS information seeking | Unadj. | — | — | — | — | .371 | <.001 |
|
Adj. | — | — | — | — | .284 | <.001 |
R2 adjusted |
|
.157 |
|
.143 |
|
.278 |
|
Standardized total, direct and indirect path coefficients for model (see
|
Standardized Total effects | Standardized Direct effects | Standardized Indirect effects | ||||||
Parameter estimate | Est.a | CI |
|
Est. | CI |
|
Est. | CI |
|
GCI – Social costs of information seeking | –.272 | (–.358 - |
.032 | –.272 | (–.358 - |
.032 | — | — | — |
GCI – Network expertise access | .271 | (.141-.388) | .011 | .271 | (.141-.388) | .011 | — | — | — |
GCI – Community relevance | .356 | (.246-.463) | .009 | .356 | (.246-.463) | .009 | — | — | — |
GCI – Incidental information acquisition | .378 | (.257-.476) | .011 | .378 | (.257-.476) | .011 | — | — | — |
GCI – Information seeking frequency | .301 | (.174-.382) | .018 | .301 | (.174-.382) | .018 | — | — | — |
GCI – Information use | .300 | (.234-.361) | .013 | — | — | — | .300 | (.234-.361) | .013 |
Social costs of information seeking – Network expertise access | –.293 | (–.386 - |
.020 | –.293 | (–.386 - |
.020 | — | — | — |
Social costs of information seeking – Information use | –.053 | (–.095 - |
.007 | — | — | — | –.053 | (–.095 - |
.007 |
Network expertise access – Information use for decision making | .181 | (.097-.273) | .008 | .181 | (.097-.273) | .008 | — | — | — |
Community relevance – Information use for decision making | .261 | (.160-.386) | .005 | .261 | (.160-.386) | .005 | — | — | — |
Incidental information acquisition |
.198 | (.072-.285) | .021 | .198 | (.072-.285) | .021 | — | — | — |
Information seeking frequency |
.276 | (.169-.373) | .012 | .276 | (.169-.373) | .012 | — | — | — |
a Est. = estimate.
HIV/AIDS information sharing.
Categories | Concepts | Sample participant quotations |
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Looking out for each other | “ |
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Working together | “ |
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Making a difference | “ |
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Informing community | “… |
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Organizing events | “… |
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Disseminating messages | “… |
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Encouraging safety | “… |
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Referring and recommending | “… |
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Information seeking | “ |
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Countering stigmatization and the social costs of information seeking | “… |
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Incidental information acquisition | “ |
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Network expertise accessibility | “ |
|
Information use | “… |
Results of this study support our central premise that HIV/AIDS information interaction and gay community involvement are related among YMSM. Gay community involvement was a significant predictor of all HII-related variables included in the study: social costs, community relevance, network expertise access, incidental information acquisition, information seeking, and information use. The overall model also predicted a non-trivial, although modest, amount of the variance in information acquisition frequency (9-14%) and information use (28%). Moreover, community-related variables alone explained 17% of the variance in information use. Community-related variables were also stronger predictors of HII than demographics. Furthermore, our data offer insight into
Our findings suggest that HIV/AIDS-related HII and associated technology uses are community-embedded processes, yet the majority of HIV/AIDS-related informatics interventions to date attempt to influence individual-level constructs, such as knowledge, attitudes, and self-efficacy [
Our model is strengthened by inclusion of theoretical mediators that help explain the effect of community involvement on information acquisition and use. Therefore, we offer the first quantitative assessment of important concepts that have emerged from qualitative field work in information science, such as social costs of information seeking and collective relevance (eg, [
Our findings raise questions about the potential role of information interaction in observed relationships between gay community involvement and HIV risk behavior. MSM who are more involved in the community have more sexual partners [
While our research focuses on YMSM and gay community involvement, our findings may have relevance for other illnesses and community contexts, since prior research in other contexts has shown that communities may vary widely with regard to media and community organization involvement in health communication [
The overall finding that YMSM with greater involvement in the gay community used the Internet more resonates with research conducted in the general adult American population. Internet communication facilitates maintenance of a wide range of geographically dispersed relationships [
Although the purpose of the study was to identify whether and how much community involvement predicted human-information acquisition, the overall magnitude of prediction for information seeking and incidental acquisition were relatively low (
This research showed that, in a web-based sample of young MSM, gay community involvement was a significant predictor of a series of HIV/AIDS–related information interaction and technology use variables. Moreover, our model demonstrated that greater information use was predicted by social costs of information seeking, perceived community relevance, and network expertise accessibility. We also highlight the potential importance of a new variable, information sharing, at the community-HII nexus. Our findings suggested partial support for our hypothesis that YMSM who were more involved in the gay community would make heavier use of technologies to socialize with others. Together, these findings suggest that HIV/AIDS information interaction and technology use should be conceptualized as community-embedded processes as well as individual ones. Such recognition highlights the potential for novel, community-level health informatics interventions, while allowing us to perceive informational dynamics underlying community life that we did not see before.
AIDS Service Organization
consumer health informatics
gay community involvement
human-information interaction
incidental HIV/AIDS information acquisition
information technology
Men who have sex with men
people with HIV/AIDS
young men who have sex with men
This project was funded by the National Institutes of Health, National Center for Research Resources, Grant UL1RR024986. Dr. Bauermeister is supported by a Career Development Award from the National Institute of Mental Health (K01-MH087242). Views expressed in this manuscript do not necessarily represent the views of the funding agencies.