Digital Self-Monitoring, Bodied Realities: Re-Casting App-Based Technologies in First Episode Psychosis
Bibliographic record
Abstract
Smartphone technology has seen expanding interest across nearly all areas of medicine, including psychiatry. This paper discusses the burgeoning use of digital technologies for symptom monitoring in the field of first episode psychosis. Drawing on Foucauldian theory as well as intersectional feminist materialist and critical disabilities scholarship in science and technology studies (STS), we trace a novel landscape of technologies of the self. We explore the discursive strategies that position first episode psychosis and digital technology as progressive, curative paradigms and utilize our own ethnographic work within the field of first episode psychosis to consider how lived experience is transformed within and through digital technologies. We trouble the unfettered enthusiasm for digital technologies in first episode psychosis in light of how these transformations can be understood within a larger neoliberal political rationality and demarcate the importance of having intersectional feminist STS scholarship attend to this burgeoning field.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| 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".