The 6 Dimensions of Promising Practice for Case Managed Supports to End Homelessness
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: Homelessness is a social condition increasing in frequency and severity across Canada. Interventions to end and prevent homelessness include effective case management in addition to an affordable housing provision. Little standardization exists for service providers to guide their decision making in developing and maintaining effective case management programs. The purpose of this 2-part article is to articulate dimensions of promising practice for case managers working in a "Housing First" context. Part 1 discusses research processes and findings and Part 2 articulates the 6 dimensions of quality. PRIMARY PRACTICE SETTING: Practice settings include community-based organizations that employ and support case managers whose primary role is moving people from homelessness into permanent supportive housing. FINDINGS/CONCLUSIONS: Six dimensions of promising practice are critically important to reducing barriers, improving sector collaboration, and ensuring that case managers have appropriate and effective training and support. Dimensions of promising practice are (1) collaboration and cooperation-a true team approach; (2) right matching of services-person-centered; (3) contextual case management-culture and flexibility; (4) the right kind of engagement-relationships and advocacy; (5) coordinated and well-managed system-ethics and communication; and (6) evaluation for success-support and training. IMPLICATIONS FOR CASE MANAGEMENT PRACTICE: Effective, coordinated case management, in addition to permanent affordable housing has the potential to reduce a person's or family's homelessness permanently. Organizations and professionals working in this context have the opportunity to improve processes, reduce burnout, collaborate and standardize, and, most importantly, efficiently and permanently end someone's homelessness with the help of dimensions of quality for case management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".