Recruiting under-represented populations into psychiatric research: Results from the help for hoarding study

'A more human approach … I haven't found that really': experiences of hoarding difficulties and seeking help

BACKGROUND AND AIMS

People with hoarding behaviours often struggle to engage in treatment. This study aimed to explore the experiences of a sample of people who identify as engaging in hoarding behaviours and who are seeking support. Exploring motivation to seek help, the barriers those who hoard face in accessing support and what facilitates accepting help, can aid understanding of how best to intervene.

METHOD

Eight individuals who self-identified as seeking help in relation to hoarding behaviours were recruited via social media and support groups. Interviews were conducted by telephone or video call, before being transcribed and analysed using interpretative phenomenological analysis.

RESULTS

Participants described complex help-seeking narratives and reported continued ambivalence about addressing their hoarding behaviours. The four group experiential themes identified were Wrestling with identity; Who can I trust?; Services don't fit; and Being overlooked: 'they're too busy looking at the thing, not the person'. Difficulties trusting others and services were identified; services were experienced as rejecting and many participants sought help for problems other than their hoarding. Problems accessing appropriate help for hoarding were predominant in the narratives, although participants who had accessed peer support described this as valuable.

CONCLUSIONS

There are both internal (e.g. fear of judgement; feeling overwhelmed) and external (e.g. service gaps) barriers that make finding useful help for hoarding behaviours very difficult. Services may facilitate those seeking help by taking a compassionate and person-centred approach to hoarding problems.

Co-development of a Best Practice Checklist for Mental Health Data Science: A Delphi Study

Background: Mental health research is commonly affected by difficulties in recruiting and retaining participants, resulting in findings which are based on a sub-sample of those actually living with mental illness. Increasing the use of Big Data for mental health research, especially routinely-collected data, could improve this situation. However, steps to facilitate this must be enacted in collaboration with those who would provide the data - people with mental health conditions. Methods: We used the Delphi method to create a best practice checklist for mental health data science. Twenty participants with both expertise in data science and personal experience of mental illness worked together over three phases. In Phase 1, participants rated a list of 63 statements and added any statements or topics that were missing. Statements receiving a mean score of 5 or more (out of 7) were retained. These were then combined with the results of a rapid thematic analysis of participants' comments to produce a 14-item draft checklist, with each item split into two components: best practice now and best practice in the future. In Phase 2, participants indicated whether or not each item should remain in the checklist, and items that scored more than 50% endorsement were retained. In Phase 3 participants rated their satisfaction with the final checklist. Results: The final checklist was made up of 14 "best practice" items, with each item covering best practice now and best practice in the future. At the end of the three phases, 85% of participants were (very) satisfied with the two best practice checklists, with no participants expressing dissatisfaction. Conclusions: Increased stakeholder involvement is essential at every stage of mental health data science. The checklist produced through this work represents the views of people with experience of mental illness, and it is hoped that it will be used to facilitate trustworthy and innovative research which is inclusive of a wider range of individuals.