Assessing the impact of psychiatric genetic counseling on psychiatric hospitalizations
Bibliographic record
Abstract
Psychiatric genetic counseling (pGC) can improve patient empowerment and self-efficacy. We explored the relationship between pGC and psychiatric hospitalizations, for which no prior data exist. Using Population Data BC (a provincial dataset), we tested two hypotheses: (1) among patients (>18 years) with psychiatric conditions who received pGC between May 2010 and Dec 2016 (N = 387), compared with the year pre-pGC, in the year post-pGC there would be fewer (a) individuals hospitalized and (b) total hospital admissions; and (2) using a matched cohort design, compared with controls (N = 363, matched 1:4 for sex, diagnosis, time since diagnosis, region, and age, and assigned a pseudo pGC index date), the pGC cohort (N = 91) would have (a) more individuals whose number of hospitalizations decreased and (b) fewer hospitalizations post-pGC/pseudo-index. We also explored total days in hospital. Within the pGC cohort, there were fewer hospitalizations post-pGC than pre- pGC (p = 0.011, OR = 1.69), and total days in hospital decreased (1085 to 669). However, when compared to matched controls, the post-pGC/pseudo index change in hospitalizations among pGC cases was not statistically significant, even after controlling for the higher number of hospitalizations prior. pGC may lead to fewer psychiatric hospitalizations and cost savings; further studies exploring this are warranted.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".