Acceptability and Feasibility of Using a Novel Geospatial Method to Measure Neighborhood Contexts and Mobility Among Transgender Women in New York City
Bibliographic record
Abstract
Purpose: To date, no studies utilizing global positioning system (GPS) technologies to measure mobility and environmental exposures have been conducted among a sample of transgender women despite the potential salient role neighborhood contexts may play in the health of this population. As such, the purpose of this study was to assess the acceptability and feasibility of a weeklong GPS protocol among a sample of transgender women in New York City. Methods: A sample of 14 transgender women residing in the New York City metropolitan area were recruited through community based methods to wear and charge a GPS device for 7 days to measure daily mobility. The acceptability of these methods was assessed using a pre- and postprotocol survey and their feasibility was measured using objective data derived from the GPS device. Pre- and postprotocol survey measures were compared using McNemar's test. Results: Participants reported high ratings of preprotocol acceptability, as well as few concerns regarding safety, appearance, and losing the device, all of which were maintained after completing the protocol. All 14 devices that were distributed were returned. In addition, all 14 participants had GPS data for at least 1 h on 1 day, and nine participants (64.3%) had at least 8 h of GPS data on all days. Conclusion: The findings of this pilot study demonstrate that the GPS methods are both acceptable and feasible among this sample of transgender women. GPS devices may be used in research among transgender women to understand neighborhood determinants of HIV and other STIs.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".