The collaborative management of antipsychotic medication and its obstacles: A qualitative study
Bibliographic record
Abstract
© 2020 Elsevier Ltd Antipsychotic medication is the primary treatment for psychotic conditions such as schizophrenia and schizoaffective disorders; nevertheless, its administration is not free from conflicts. Despite taking their medication regularly, 25–50% of patients report no benefits or perceive this type of treatment as an imposition. Following in the footsteps of a previous initiative in Quebec (Canada), the Gestion Autonome de la Médication en Santé Mentale (GAM), this article ethnographically analyses the main obstacles to the collaborative management of antipsychotics in Catalonia (Spain) as a previous step for the implementation of this initiative in the Catalan mental healthcare network. We conducted in-depth interviews with patients (38), family caregivers (18) and mental health professionals (19), as well as ten focus groups, in two public mental health services, and patients' and caregivers' associations. Data were collected between February and December 2018. We detected three main obstacles to collaboration among participants. First, different understanding of the patient's distress, either as deriving from the symptoms of the disorder (professionals) or the adverse effects of the medication (patients). Second, differences in the definition of (un)awareness of the disorder. Whereas professionals associated disorder awareness with treatment compliance, caregivers understood it as synonymous with self-care, and among patients “awareness of suffering” emerged as a comprehensive category of a set of discomforts (i.e., symptoms, adverse effects of medication, previous admissions, stigma). Third, discordant expectations regarding clinical communication that can be condensed in the differences in meaning between the Spanish words “trato” and “tratamiento”, whe
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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 | 0.021 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.014 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".