Evaluation of a barriers to discharge screening in a mental health impatient unit
Bibliographic record
Abstract
BACKGROUND: People admitted to inpatient mental health units have a variety of social problems such as homelessness, lack of funding or poor social support. This can be reflected in longer length of stay. The goal of the study is to assess the predictive validity of the Barriers to Discharge questionnaire scores on length of stay in a Mental Health inpatient unit.METHODS: Two thousand three hundred fifty-five inpatients were analyzed with a mean age of 38.9±16.0 years, 51% were male and the mean log of length of stay in days was 1.23±1.11. The barrier to discharge score continuous variable was transformed in categorical variable, and the categories were fixed as follows: 0-121 points: low risk; 122-199 points: medium-low risk; 200-274 points: medium-high risk; 275-817 points high risk, according to the definition of the creators of the questionnaire. A multiple linear regression model was used with log of Length of stay in days as dependent variable.RESULTS: People belonging to the “low risk” category showed a significantly lower length of stay compared to the other higher risk categories.CONCLUSIONS: The Barriers to Discharge tool, although promising, needs to be tested in other contexts and using mental health diagnosis as variable, to see whether it can be a useful predictor of severity of people suffering from mental problems, which is reflected in the length of stay in an inpatient unit.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".