Cultural pathways to psychosis care: Patient and caregiver narratives from Puebla, Mexico
Bibliographic record
Abstract
The current study used the McGill Illness Narrative Interview (MINI) to explore patients’ ( n = 6) and caregivers’ ( n = 3) narratives about how they identified and sought care for psychosis. Participants were recruited from an outpatient clinic at the Hospital Psiquiátrico Dr. Rafael Serrano , a public psychiatric hospital in Puebla, Mexico. All participants consented to complete semi-structured interviews in Spanish. Thematic analyses were used to inductively identify common themes in participants’ narratives. The results indicated that during the initial symptom onset, most participants noticed the presence of hallucinations but did not seek help for this hallmark symptom. Participants described seeking care only when they or their ill relative exhibited escalating aggressive behaviors or physical symptoms that were interpreted as common medical problems. As participants became connected to specialty mental health services, they began to develop a conceptualization of psychosis as a disorder of aggression. For some participants, this conceptualization of psychosis as an illness of aggression contributed to their ambivalence about the diagnosis. These results can be understood using a cultural scripts framework, which suggests that cultural norms are influenced by collective understandings of normalcy and valorization of behaviors. Implications for community campaigns are discussed.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".