User Perceptions of ¡Protéjase!: An Intervention Designed to Increase Protective Equipment Use Among Mexican Immigrant and Mexican American Farmworkers
Bibliographic record
Abstract
BACKGROUND: Farmworkers' exposures to pesticides are reduced when they wear personal protective equipment (PPE), and mobile health (mHealth) platforms can potentially deliver information to farmworkers to help promote PPE use. However, little is known about the feasibility of using mHealth platforms to promote farmworkers' use of PPE. OBJECTIVE: The objective of the study was to describe the development and feasibility-testing of Protect Yourself! (¡Protéjase!), an intervention designed to increase PPE use. As the vast majority of farmworkers in the United States are from Mexico, we examined the intervention in a primarily Mexican-origin farmworker population. METHODS: ¡Protéjase was developed in several steps. First, we performed ethnographic observations to understand what prevents PPE use. Next, we developed program components that met the challenges uncovered in the ethnographic observations, seeking direct feedback from farmworkers on each component. Feasibility was assessed using surveys and focus groups. Material was provided in Spanish or English at the preference of the participant. Finally, we pilot tested each component of the intervention, including: (1) PPE that was provided to each worker for their personal use during the intervention trial, and (2) delivery of an application-based tool that promoted the use of PPE through daily individualized messaging. RESULTS: 55 farmworkers enrolled in the study, but only 41 of 55 (75%) completed the entire pilot intervention trial. Results focus on the evaluation of the intervention, and include only those who completed the entire trial. Among farmworkers who completed the entire intervention trial, all but two farmworkers were born in Mexico and were Spanish speaking. Still, all study participants self-identified as Mexican or Mexican-American. When asked what changes were needed in the intervention's messaging or delivery to increase user satisfaction, 22 out of 41 participants (54%) felt that no changes were needed. However, 16 of 41 participants (39%) suggested small changes to messaging (eg, refer to long pants as pants only) to improve their understanding of the messages. Finally, a small number (3 of 41 participants, 7%) felt that messages were difficult to read, primarily due to low literacy. CONCLUSIONS: The ¡Protéjase! mHealth program demonstrated very good feasibility, satisfaction, and acceptance; potential improvements (eg, small modifications in messaging to increase farmworkers' use) were noted. Overall, the PPE provided to workers as well as the mHealth platform were both perceived as useful for promoting PPE use.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".