All the voices we cannot hear: a taxonomy of why some populations' experiences are missing from health and care quality evidence and the Toolkit for Assessing Under Representation in User Surveys (TAURUS).

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Tác giả: Chris Graham, Jenny King, Clare Lerway, Alan J Poots

Ngôn ngữ: eng

Ký hiệu phân loại: 127 The unconscious and the subconscious

Thông tin xuất bản: England : BMJ open , 2025

Mô tả vật lý:

Bộ sưu tập: NCBI

ID: 680914

BACKGROUND: Patient and public voices are vital for understanding the quality of health and care. However, many healthcare providers, commissioners, decision-makers and researchers cannot hear the voices of all people within diverse populations, with different groups excluded from patient experience data collections and analyses for a variety of causes-some of which are overlooked or misunderstood. Exclusion and under-representation can be particularly problematic for disadvantaged people and marginalised communities, and risk exacerbating existing inequalities. KEY MESSAGES: We posit a taxonomy of causes of exclusion and under-representation in research involving patient and public voice: (1) Non-access: people are excluded because they cannot or do not access a service in the first place. (2) Non-invitation: health research and feedback programmes may not include invitations for some groups, despite being eligible, or use language that is inappropriate. (3) Non-response: some communities are less likely to respond to requests for feedback. (4) Non-identification: sometimes the structure and content of data do not allow the identification of distinct groups in data collections. (5) Non-review: sometimes data are available and yet not analysed. We provide a CONCLUSIONS: These causes result in under-representation that creates knowledge gaps for quality and equity. Overcoming this requires strategic approaches with a commitment to equity and inclusion, supported by resources in collecting and using data with an appropriate range of methodologies. Providers should undertake equalities impact assessments around new data collections, using the taxonomy to identify and minimise potential sources of under-representation and ensure that voices are heard and acted on.
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