M246. DIGITAL SELF-MONITORING AND EMBODIMENT IN FIRST EPISODE PSYCHOSIS: ETHICAL CONSIDERATIONS
Notice bibliographique
Résumé
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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Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,131 | 0,232 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,013 | 0,027 |
| Communication savante | 0,009 | 0,007 |
| Science ouverte | 0,003 | 0,007 |
| Intégrité de la recherche | 0,016 | 0,015 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».