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'Sheds for Life': delivering a gender-transformative approach to health promotion in Men's Sheds.

Aisling McGrathNiamh MurphyNoel Richardson
Published in: Health promotion international (2022)
Research has highlighted the importance of gendered approaches to engage men with health. Sheds for Life (SFL) is a health and wellbeing initiative that utilizes evidence-based and gender-specific approaches to engage hard to reach men with health promotion directly in the Men's Sheds (Sheds) setting. To understand the impact of SFL and how participants (Shedders) experienced SFL in practice, this qualitative study applied a framework of constructivism and aimed to explore how gendered approaches impacted engagement with SFL through Shedder's own accounts of their attitudes, opinions and experiences. Qualitative methods incorporating ethnographical observations, focus groups (n = 8) and short semi-structured interviews (n = 19) were conducted with SFL participants in the Shed setting. Reflexive thematic analysis was used to analyse the data to faithfully capture Shedders' experiences while acknowledging the reflexive influence of the researcher. Findings led to three key themes: Creating the 'right environment'; Normalizing meaningful conversations; a legacy for 'talking health' with subthemes of creating safety and trust and strengthening of bonds; and transforming perceptions of how men 'do health' with subthemes of reaping the benefits of engaging with health and reframing attitudes towards health. This is first study to capture Shedders' experiences of a structured health promotion initiative in the Shed setting. Findings highlight the value in utilizing the Shed setting to engage men with health and the importance of gender-specific strategies which encourage a gender-transformative approach to men's health promotion.
Keyphrases
  • health promotion
  • mental health
  • healthcare
  • public health
  • middle aged
  • health information
  • primary care
  • systematic review
  • machine learning
  • risk assessment
  • electronic health record
  • deep learning