Acceptability of smartphone applications for global positioning system (GPS) and ecological momentary assessment (EMA) research among sexual minority men
Bibliographic record
Abstract
BACKGROUND: Emerging research is using global positioning system (GPS) and ecological momentary assessment (EMA) methods among sexual minority men (SMM), a population that experiences multiple health disparities. However, we are not aware of any research that has combined these approaches among SMM, highlighting the need for acceptability and feasibility research. The purpose of this study was to examine the acceptability of implementing GPS and EMA research protocols using smartphone applications among SMM as well as related socio-demographic correlates. METHODS: Data come from a sample of SMM on a popular geosocial-networking app in Paris, France (n = 580). We assessed the acceptability of implementing GPS and EMA research protocols on smartphone apps as well as socio-demographic characteristics (i.e., age, sexual orientation, country of origin, employment status, and relationship status). We examined the anticipated acceptability of GPS and EMA data collection methods as well as socio-demographic correlates of acceptability of GPS and EMA methods. RESULTS: We found that over half (54.1%) of the sample was willing to download a smartphone app for GPS-based research and we found that almost 60% of the participants were willing to download a smartphone app for EMA-based research. In total, 44.0% reported that they were willing to download both GPS and EMA apps. In addition, we found that older participants were less willing to download a smartphone app for EMA research than younger participants aged 18-24 (40-49 years: aPR = 0.40; 95% CI = 0.20, 0.78) and students were more willing to download smartphone apps for both GPS and EMA research (aPR = 1.41; 95% CI = 1.02, 1.95). CONCLUSION: Results from this study suggest that using smartphone apps to implement GPS and EMA methods among some SMM are acceptable. However, care should be taken as segments of SMM are less likely to be willing to engage in this type of research.
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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 | 0.030 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".