The Essential and Potentially Inappropriate Use of Antipsychotics across Income Groups: An Analysis of Linked Administrative Data
Bibliographic record
Abstract
OBJECTIVE: To examine the essential and potentially inappropriate use of antipsychotics across income groups. METHOD: Linked health, pharmaceutical use, and income data from British Columbia were analyzed to examine antipsychotic use in 2 study cohorts. In the first cohort, the essential use of antipsychotics was assessed among adults who had a recorded diagnosis of schizophrenia in a 2-year period, 2004-2005. In the second cohort, potentially inappropriate use of antipsychotics was examined in people with no recorded diagnosis of schizophrenia or bipolar disorders in 2004-2005. The second cohort was also composed exclusively of seniors with a dementia-related diagnosis who are either in long-term care or living in the community. Income-related differences in antipsychotic use in these 2 cohorts were assessed using logistic regression, controlling for health and sociodemographic characteristics known to influence medicine use. RESULTS: Among adults, the prevalence of essential antipsychotic use was high (85%), with higher odds of use evident among those in the middle-income group. Among seniors, the prevalence of potentially inappropriate antipsychotic treatment is 23%, with prevalence higher in long-term care (56%) than in the community (13%). No income-related differences were found in long-term care; however, in the community, higher odds of use were found in low-income seniors. CONCLUSION: People from low-income households have slightly lower levels of essential antipsychotic use and are more likely to receive potentially inappropriate antipsychotic treatment.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".