| Literature DB >> 34977230 |
Nick Haslam1, Jesse S Y Tse1, Simon De Deyne1.
Abstract
Some aspects of psychiatrization can be understood as forms of concept creep, the progressive expansion of concepts of harm. This article compares the two concepts and explores how concept creep sheds light on psychiatrization. We argue that although psychiatrization is in some respects a broader concept than concept creep, addressing institutional and societal dimensions of the expanding reach of psychiatry in addition to conceptual change, concept creep is broader in other respects, viewing the expansion of psychiatric concepts as examples of the broadening of a more extensive range of harm-related concepts. A concept creep perspective on psychiatrization clarifies the different forms of expansion it involves, the centrality of harm to it, its benefits as well as its costs, its variations across individuals and groups, and the drivers of psychiatrization in the general public and in fields beyond psychiatry.Entities:
Keywords: concept creep; diagnosis; harm; over-diagnosis; psychiatric classification
Year: 2021 PMID: 34977230 PMCID: PMC8716590 DOI: 10.3389/fsoc.2021.806147
Source DB: PubMed Journal: Front Sociol ISSN: 2297-7775
Selected contrasts been the concepts of psychiatrization and concept creep.
| Psychiatrization | Concept creep | |
| Primary focus | Expanding reach of psychiatric institutions, practices and concepts | Semantic inflation of harm-related concepts |
| Disciplinary home | Medical sociology | Psychology |
| Explanatory emphasis | Institutional influences | Cultural influences |
| Domain of relevance | Psychiatry and mental illness | Concepts of harm, including psychiatric concepts |
FIGURE 1Word association egograph for “well-being”. Colors indicate distinct clusters of meaning extracted using walktrap clustering. Cluster size indicates the prevalence of association responses measured as response in-strength (i.e., the sum of weighted incoming edges).
FIGURE 2Word association egograph for the word “mental health”. Colors indicate distinct clusters of meaning extracted using walktrap clustering. Cluster size indicates the prevalence of association responses measured as response in-strength (i.e., the sum of weighted incoming edges).