Using the Think-Aloud Method to Assess the Feasibility and Acceptability of Network Canvas Among Black Men Who Have Sex With Men and Transgender Persons: Qualitative Analysis
Bibliographic record
Abstract
BACKGROUND: Characteristics of an individual's social network have been important factors in understanding infectious disease transmission patterns. Social network data collection is generally time and resource intensive, yet it is crucial to our understanding of the complex epidemiologic landscape of human behaviors among stigmatized social groups. OBJECTIVE: We sought to evaluate the feasibility and acceptability of a self-administered social network data collection tool, Network Canvas, among Black men who have sex with men (BMSM) and transgender persons using the think-aloud method, which is a robust and flexible research technique used to perform usability testing. METHODS: We piloted a self-administered network interview within the Network Canvas Software Suite. Participants aged 18 years and older were recruited through a community-based organization in Atlanta, GA, and were included based upon their willingness to share information on sexual behaviors and drug use for themselves and their social networks. A semistructured interview guide was used to document cognitive decision-making processes while using the tool. Recorded interviews were transcribed verbatim, and thematic analyses were performed. RESULTS: Among 7 BMSM and transgender participants, three main themes were identified from cognitive processes: (1) the utility, (2) navigation, and (3) intuitive design of Network Canvas. Overall, Network Canvas was described as "easy to use," with suggestions mainly directed toward improving navigation tools and implementing an initial tutorial on the program prior to use. Participants were willing to use Network Canvas to document their social networks and characteristics. In general, observed verbal responses from participants matched their behavior, although there were some discrepancies between verbal affirmations of use and understanding versus external observation. CONCLUSIONS: We found Network Canvas to be a useful new tool to capture social network data. Self-administration allowed participants the opportunity to provide sensitive information about themselves and their social networks. Furthermore, automated name generation and visualization of an individuals' social network in the app has the potential to reduce cognitive burden during data collection. More efficient methods of social network data collection have the potential to provide epidemiologic information to guide prevention efforts for populations with stigmatized health conditions or behaviors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".