The development and validation of a marginalization index for inpatient psychiatry
Bibliographic record
Abstract
BACKGROUND: Marginalization is a multidimensional social construct that influences the mental health status of individuals and their use of psychiatric services. However, its conceptualization and measurement are challenging due to inconsistencies in definitions, and the lack of standard data sources to measure this construct. AIMS: To create an index for screening marginalization based on an existing comprehensive assessment system used in inpatient psychiatry. METHOD: Items anticipated to be indicative of marginalization were identified from the Resident Assessment Instrument-Mental Health (RAI-MH) that is used in all inpatient mental health beds in Ontario, Canada. Principal Component Analysis (PCA) and cluster analysis of these items was performed on a sample of 81,232 patients admitted into psychiatric care in Ontario between 1 January 2011 and 31 December 2016 to identify dimensions being measured. Various weights and scoring methods were tested to assess convergent validity on multiple outcomes of marginalization. Receiver Operating Characteristic (ROC) curve analysis was utilized to determine optimal cut-offs for the index by modeling the likelihood of different marginalization outcomes, including homelessness. RESULTS: Fifteen items were identified for the development of the Marginalization Index (MI). PCA and cluster analysis identified that the items measured five dimensions. ROC curve analysis among homeless individuals identified an Area Under the Curve of 0.76 and an optimal cut-off of five on the MI. Frequency analysis of the index by different characteristics identified homeless individuals, frequent mental health service users, persons with a history of violence and police intervention, and persons with addictions issues, as groups with the highest scores, confirming the convergent validity of the index. CONCLUSION: The MI is a valid measure of marginalization and is strong predictor of risk of homelessness among psychiatric inpatients. MI provides a resource that can be used for social and health policy, decision-support and evaluation.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".