M246. DIGITAL SELF-MONITORING AND EMBODIMENT IN FIRST EPISODE PSYCHOSIS: ETHICAL CONSIDERATIONS
Bibliographic record
Abstract
Abstract Background Smartphone technology has seen expanding interest across nearly all areas of medicine, including psychiatry, where app-based technologies frequently function as proxies for digitized behavioural phenotypes (Firth and Torous 2015). In the area first episode psychosis especially, there has been a rising interest in the use of digital platforms for patient self-management as well as for assessment of symptom domains (Ben-Zeev et al. 2014; Bell et al. 2018). Methods This paper discusses findings from a 3 year-long ethnographic study carried out within a first episode psychosis program in Toronto, Canada, in combination with a discourse analysis of the clinical and research literature relating to the use of self-monitoring technologies within first episode psychosis contexts. The qualitative data consists of formal and informal interviews with psychiatric service users, family members, and clinicians (n=45 interviews), in addition to observational field work within the clinical setting. Data were analyzed thematically within an interpretivist-constructivist frame, and triangulated through reflexive field notes, member-checking, and the authors’ clinical experience within the field. Themes were reviewed with senior clinicians in the first episode clinic setting as well as psychiatric service users for reliability and fidelity. Results Self-monitoring technologies are increasingly used in both research and clinical care settings, most frequently related to the management of medication side effects and the tracking of the phenomenological aspects of psychotic and psychotic-like experiences. The uptake of these technologies by psychiatric service users in this setting was varied: at times, symptom and side effect tracking faciliated conversations about uncomfortable topics such as sexual side effects of antipsychotics, while in other instances the use of self-monitoring technologies was intrusive. Challenges with self-monitoring were likely to arise when issues relevant to understanding complex phenomena such as medication adherence were prematurely narrowed or when experiential narratives were foreclosed by the structure imposed by the technologies themselves. Resistance to self-monitoring arose in novel and interesting ways. Discussion Engaging in self-monitoring, whether for side effect tracking or phenomenological analysis of moment-to-moment experiences have significant material effects on those engaged in such practices. When we consider the study findings through the lens of contemporary mental health ethics, we can appreciate how such technologies carry with them a potential for what has been termed “epistemic injustice” (Medina 2013; Fricker 2007), even at the same time as they offer potentially novel, meaningful forms of health care engagement. Grappling with this tension is of critical importance in light of the expansion of such platforms in both clinical and research environments
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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 | 0.131 | 0.232 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.027 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.016 | 0.015 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".