SU110. Meaning-Making, Self-identity, and Ambivalence Around Antipsychotics in First-Episode Psychosis
Bibliographic record
Abstract
Background: An issue throughout all areas of medicine, nonadherence to medications is particularly challenging in first-episode psychosis (FEP), where discontinuation of medications has been found to be one of the most reliable predictors of relapse after symptom remission (Haddad et al, 2014; Alvarez-Jimenez et al, 2012). And while medication factors and patient expectancies are frequently employed to understand challenges surrounding adherence, there are limits to their explanatory power. Methods: Utilizing ethnographic methodologies, we collected and analyzed narratives of FEP clinic users in order to explore relationships between experiences of illness, contacts with the mental health system, and medication nonadherence. Data were composed of text materials relating to early intervention and first-episode psychosis and antipsychotic medications, longitudinal key informant interviews, and participant observation in a FEP clinic setting in Toronto, Canada. An interpretive thematic analysis of interview transcripts, field notes, and texts was subsequently undertaken, and emerging themes developed iteratively through multiple readings of the texts; the use of multiple coders, member checks, and the range of data sources enabled triangulation of the established themes. Results: The imperative to consistently ingest or inject psychiatric medications can, at times, clash with the lived experience of those struggling to reorient a sense of self in the aftermath of a psychotic illness. Frictions exist between subjective meanings attached to experiences of psychosis and their biomedical framing, leading to ruptures in the clinical setting that are dramatized around medication use. Conclusion: This pilot study demonstrates that a much broader scope of subjective and intersubjective experience is salient to issues surrounding medication use in first-episode psychosis. Moreover, meanings attached to medications are continually shifting, imbued with ambivalence, and overdetermined. As such, limiting analyses of antipsychotic adherence to medication-centric factors and cross-sectional methodologies fail to encapsulate the breadth of relevant meanings and experiences to medications and their mutable nature. These findings offer insights that may further facilitate engagement of individuals around medications and within first-episode services more broadly.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.026 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".