Case 6 : No Fixed Address: A Cost-Effectiveness Analysis of a Program to Prevent Psychiatric Discharge to Homelessness
Bibliographic record
Abstract
Najwa D'Souza, the CEO of Hampden Health Care, is looking for an intervention that will reduce the rate of discharge to homelessness from the psychiatric units at her hospitals. She is presented with the No Fixed Address (NFA) program as a possible solution. This intervention is a multipronged, hospital-based intervention that provides support to psychiatric clients who are either experiencing homelessness or are at risk of homelessness. To implement the program at Hampden Health Care, hospital staff would refer clients they suspect are at risk of being discharged to homelessness to the NFA program. From there, clients would have the option of receiving streamlined Ontario Works support or seeing a housing advocate who is employed by the local shelter system, Hampden Community House. Excited about the possibilities this intervention holds, Najwa must conduct an economic evaluation to assess the NFA program’s value for money, and support decision making for Hampden Health Care and other relevant stakeholders. Students take the perspective of someone on Najwa’s health economics team. They are given a list of parameters including the types, quantities, and costs per unit of the resources needed for the two interventions being compared (usual care versus NFA program). Students must incorporate the parameters into a model-based economic evaluation comparing the costs and consequences of the alternative interventions. Students are then tasked with working through the rest of the steps to complete a cost-effectiveness analysis.
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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 | 0.018 | 0.083 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".