Applying quality improvement to clinical practice: primer for psychiatrists
Bibliographic record
Abstract
SUMMARY Quality improvement (QI) is an evidence-based approach to analysing and improving healthcare systems. QI's success has led it to become a required competency expected of medical professionals in several countries. However, much of the QI literature to date has not focused on mental health. Moreover, many psychiatrists have no formal training in QI. To address this gap, this article introduces key QI concepts, including six dimensions of quality care, the Model for Improvement and plan–do–study–act cycles. Each QI concept is illustrated using a fictitious case study of an out-patient psychiatrist reducing chronic benzodiazepine use in their clinic.
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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 | 0.035 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.009 | 0.031 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".