The Work of Getting Better: An Institutional Ethnography of an Early Psychosis Intervention Clinic
Bibliographic record
Abstract
The imperative to provide recovery-oriented services in mental healthcare settings has become foundational to mental health policies world-wide. However, in the context of psychiatric services, historically characterized as paternalistic and coercive, the translation of recovery policy into practice is complex, challenging and unevenly taken up. Early intervention services for young people experiencing a first episode of psychosis adhere to a recovery-oriented approach that aims to be client-centred, flexible and collaborative, empowering young people with the right and responsibility to be engaged in shared decision-making about their treatment options, which involve low-dose medication and psychosocial services. However, how these recovery practices unfold in young peoples’ interactions in early psychosis intervention (EPI) programs is not clear. In this study, I employ an institutional ethnographic approach to explore how the social organization of an EPI clinic coordinates young people’s everyday work of recovery. Methods included over 110 hours of ethnographic observation, in-depth interviews with 27 participants (18 clinic staff, four young people and five family members) and textual analysis of clinic documents (e.g., case files, administrative forms, policy reports). I provide an ethnographic description of “what actually happens” in young people’s interactions with service providers of an EPI clinic situated in Ontario, Canada. Findings detail how institutional processes shaped by the medication adherence discourse produced a disjuncture between service providers intent to provide recovery-principled care and young people’s experiences of medication uptake via institutional relations of informal coercion. In a related disjuncture of conflicting directives and objectives that govern the clinic, this analysis also outlines how the clinic manager’s and staff’s resistance to hospital rulings that impeded EPI policy principles were part of the extended sequence of activities that produced trust. These findings have important implications for shifts at the system level in terms of medication management and an emphasis from risk management and quality governance to an organizing framework based on trust.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.023 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".