Factors predicting the presence of impaired clinical insight in liaison psychiatric patients assessed in the Emergency Room
Bibliographic record
Abstract
OBJECTIVES: There are limited studies on the factors that can predict the presence of impaired clinical insight specifically in an Emergency Room (ER) psychiatric patient population. The objective of this study is to examine the factors that can predict the likelihood that a patient presenting to the ER will have impaired clinical insight. METHODS: Nineteen independent demographic and clinical factors contained on data assessment tools for 337 patients assessed by the crisis team in the ER over 6 months were compiled and analysed using SPSS Version 20 with univariate analyses and logistic regression. RESULTS: Patients who were unemployed or had a history of self-harm or had psychotic symptoms on mental state examination were about two, three and six times, respectively, more likely to have impaired clinical insight compared with those who were employed, had no history of self-harm or had no psychotic symptoms on mental state examination, controlling for other factors in the logistic regression model. CONCLUSION: Patients who are unemployed, have a history of self-harm or have psychotic symptoms following as psychiatric assessment in the ER may benefit from an insight-oriented psychotherapy.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".