Compassion in healthcare is valued by patients, healthcare professionals (HCPs), and leads to improved outcomes. Notwithstanding reports of systemic failings in the provision of compassionate care, research regarding ways to intervene remains limited. The aim of this study is to clarify compassion intervention needs in a diverse HCP workforce in public secondary healthcare in Aotearoa New Zealand (NZ) by utilising a co-design process. The co-design process involved a series of workshops with clinicians followed by in-depth interviews with healthcare leaders to derive input regarding feasibility and implementation. Reflexive thematic analysis was used to analyze the data. There was a great deal of interest in compassion interventions from healthcare professionals and leaders. However, for compassion interventions to be acceptable, feasible, and effective, compassion interventions design should be reimagined and reflected at each step of interventional design and implementation and span across organizational levels. Namely, the results of the study showed the preference for non-individual focused multi-level interventions to build bridges and connections. The desired compassion intervention components included practising connecting with others' humanity, improving compassion knowledge and relational and reflective skills, and cultural safety and anti-racism training. Experiential training embedded in models of cultural dialogue was the preferred interventional modality. Prioritising leadership as an intervention site was suggested to improve leadership's buy-in of compassion interventions and possibly serve as a starting point for transforming the broader culture, reviving interconnectedness in a healthcare system described as fragmented, disconnected, and alienating with compassion also acting as an equalizer of power.
Keyphrases
- healthcare
- physical activity
- randomized controlled trial
- palliative care
- public health
- quality improvement
- emergency department
- ejection fraction
- type diabetes
- metabolic syndrome
- weight loss
- prognostic factors
- newly diagnosed
- insulin resistance
- deep learning
- glycemic control
- big data
- data analysis
- patient reported outcomes
- drug induced