The impact of caregiver familiarity with mental disorders on timing of intervention in first‐episode psychosis
Bibliographic record
Abstract
AIM: Based on prior research, we hypothesized that personal or family familiarity with psychosis would have a different effect on pathways to care as compared to personal or family familiarity with mental disorders. METHODS: Caregivers of 32 patients receiving treatment for a first episode of psychosis at a specialized early intervention centre provided information regarding their familiarity with psychosis and mental disorders. Information on the duration of untreated psychosis (DUP) and on the duration of untreated illness (DUI) was collected from patients and their caregivers. RESULTS: Although we found a trend in the direction of lowered DUP and longer DUI for those with personal or family familiarity with psychosis, these effects were not statistically significant. A trend was found for a higher DUI for those with personal or family familiarity with mental disorders in general, but this effect was not significant. CONCLUSION: We did not find that differential familiarity with mental disorders and by extension, personal or family familiarity, affected measures of delay in treatment of a first episode of psychosis. Trends in our findings in the hypothesized directions suggest that a larger sample size may reveal significant differential effects of previous experience with mental disorders in general and psychosis in particular on delay in help seeking during different phases of the illness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".