Influence of Information and Communication Technologies on the Resilience and Coping of Sexual and Gender Minority Youth in the United States and Canada (Project #Queery): Mixed Methods Survey
Bibliographic record
Abstract
BACKGROUND: Sexual and gender minority youth are a population in peril, exemplified by their disproportionate risk of negative experiences and outcomes. Sexual and gender minority youth may be particularly active users of information and communication technologies (ICTs), and it is important to identify the potential contributions of ICTs to their resilience and well-being. OBJECTIVE: Our aim was to (1) investigate the use of ICTs by sexual and gender minority youth, (2) identify the ways that ICTs influence the resilience and coping of sexual and gender minority youth, focusing on promotion of well-being through self-guided support-seeking (particularly using mobile devices), (3) develop a contextually relevant theoretical conceptualization of resilience incorporating minority stress and ecological approaches, (4) generate best practices and materials that are accessible to multiple interested groups, and (5) identify whether video narratives are a viable alternative to collect qualitative responses in Web-based surveys for youth. METHODS: Mixed methods, cross-sectional data (N=6309) were collected via a Web-based survey from across the United States and Canada from March-July 2016. The sample was generated using a multipronged, targeted recruitment approach using Web-based strategies and consists of self-identified English-speaking sexual and gender minority youth aged 14-29 with technological literacy sufficient to complete the Web-based survey. The survey was divided into eight sections: (1) essential demographics, (2) ICT usage, (3) health and mental health, (4) coping and resilience, (5) sexual and gender minority youth identities and engagement, (6) fandom communities, (7) nonessential demographics, and (8) a video submission (optional, n=108). The option of a 3-5 minute video submission represents a new research innovation in Web-based survey research. RESULTS: Data collection is complete (N=6309), and analyses are ongoing. Proposed analyses include (1) structural equation modeling of quantitative data, (2) grounded theory analysis of qualitative data, and (3) an integrative, mixed methods analysis using a data transformation design. Theoretical and methodological triangulation of analyses integrates an interwoven pattern of results into a comprehensive picture of a phenomenon. Results will be reported in 2017 and 2018. CONCLUSIONS: This research study will provide critical insights into the emerging use of ICTs by sexual and gender minority youth and identify intervention strategies to improve their well-being and reduce risks encountered by this vulnerable population. Implications for practice, research, and knowledge translation are provided.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".