Evaluation of an Integrated Cluster Care and Supportive Housing Model for Unstably Housed Persons Using the Shelter System
Bibliographic record
Abstract
PURPOSE: To evaluate the feasibility of an integrated cluster care and supportive housing model. PRIMARY PRACTICE SETTING(S): Community shelters. METHODOLOGY AND SAMPLE: The Inner City Access Program (ICAP) is a new service delivery model employed by the Toronto Central Community Care Access Centre, which combines supportive housing services and health care for homeless, underhoused, and marginalized populations using the shelter system. We evaluated the effectiveness of the ICAP in facilitating access to health services, supporting goal-setting, and promoting interprofessional case management. Client interviews examined care goals, goal achievement, and satisfaction; staff interviews determined client-centeredness of staff-identified care goals/planning; document reviews were conducted to obtain service utilization and process data. RESULTS: Twenty clients received service during a 15-month period before implementation. This increased to 147 clients during a 16-month period post-implementation at a 60% reduction in cost/client. Results indicated that regular interdisciplinary team meetings promoted greater service delivery efficiency; greater client satisfaction was associated with goal achievement (p < .01); and a trend toward greater perceived goal achievement (as reported separately by clients and staff) and client satisfaction when staff- and client-stated goals were more closely aligned. IMPLICATIONS FOR CASE MANAGEMENT PRACTICE: The ICAP proved an efficient and cost-effective model in engaging marginalized populations. There was a trend toward greater client satisfaction when clients perceived success related to "education, employment, activities, and programs" goals.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".