Worker Preferences for a Mental Health App Within Male-Dominated Industries: Participatory Study
Bibliographic record
Abstract
BACKGROUND: Men are less likely to seek help for mental health problems, possibly because of stigma imposed by cultural masculine norms. These tendencies may be amplified within male-dominated workplaces such as the emergency services or transport industries. Mobile apps present a promising way to provide access to mental health support. However, little is known about the kinds of mental health technologies men would be willing to engage with, and no app can be effective if the intended users do not engage with it. OBJECTIVE: The goal of this participatory user research study was to explore the perceptions, preferences, and ideas of workers in male-dominated workplaces to define requirements for a mental health app that would be engaging and effective at improving psychological well-being. METHODS: Workers from male-dominated workplaces in rural, suburban, and urban locations took part in an exploratory qualitative study involving participatory workshops designed to elicit their perspectives and preferences for mental health support and the design of an app for mental health. Participants generated a number of artifacts (including draft screen designs and promotional material) designed to reify their perceptions, tacit knowledge, and ideas. RESULTS: A total of 60 workers aged between 26 and 65 years, 92% (55/60) male, from male-dominated workplaces in rural (16/60, 27%), suburban (14/60, 23%), and urban (30/60, 50%) locations participated in one of the 6 workshops, resulting in 49 unique feature ideas and 81 participant-generated artifacts. Thematic analysis resulted in a set of feature, language, and style preferences, as well as characteristics considered important by participants for a mental health app. The term "mental health" was highly stigmatized and disliked by participants. Tools including a mood tracker, self-assessment, and mood-fix tool were highly valued, and app characteristics such as brevity of interactions, minimal on-screen text, and a solutions-oriented approach were considered essential by participants. Some implementation strategies based on these findings are included in the discussion. CONCLUSIONS: Future mental health mobile phone apps targeting workers in male-dominated workplaces need to consider language use and preferred features, as well as balance the preferences of users with the demands of evidence-based intervention. In addition to informing the development of mental health apps for workers in male-dominated industries, these findings may also provide insights for mental health technologies, for men in general, and for others in high-stigma environments.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".