Uncovering Recovery: The Resistible Rise of Recovery and Resilience
Bibliographic record
Abstract
Discourses of recovery and resilience have risen to positions of dominance in the mental health field. Models of recovery and resilience enjoy purchase, in both policy and practice, across a range of settings from self-described psychiatric survivors through to mental health charities through to statutory mental health service providers. Despite this ubiquity, there is confusion about what recovery means. In this article we problematize notions of recovery and resilience, and consider what, if anything, should be recovered from these concepts. We focus on three key issues, i) individualization, ii) the persistence of a deficit model, and iii) collective approaches to recovery. Through documentary analysis we consider these issues across third sector organizations, and public and mental health policy. Firstly, definitional debates about recovery reflect wider ideological debates about the nature of mental health. The vagueness of these concepts and implicit assumptions inherent in dominant recovery and resilience discourses render them problematic because they individualize what are social problems. Secondly, these discourses, despite being seen as inherently liberatory are conceptually dependent on a notion of deficit in that talk of “positives” and “strengths” requires the existence of “negatives” and “weaknesses” for these concepts to make sense. We argue that this does little to substantially transform dominant understandings of psychological distress. Thirdly, these issues combine to impact upon the progressive potential of recovery. It comes to be seen as an individualistic experiential narrative accompaniment to medical understandings where the structural causes of distress are obscured. This in turn impacts upon the potential for recovery to be used to explore more collective, political aspects of emotional distress. Drawing on the work of Fraser, we use this critique to characterize “recovery” as a “struggle for recognition,” founded on a model of identity politics which displaces and marginalizes the need for social, political and economic redistribution to address many of the underlying causes of emotional distress. We conclude by stating that it is only when the collective, structural experiences of inequality and injustice are explicitly linked to processes of emotional distress that recovery will be possible.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.017 | 0.170 |
| Scholarly communication | 0.019 | 0.045 |
| Open science | 0.003 | 0.029 |
| Research integrity | 0.007 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".