Worker Preferences for a Mental Health App Within Male-Dominated Industries: Participatory Study
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».