Coordinated Access and Coordinated Entry System Processes in the Housing and Homelessness Sector: A Critical Commentary on Current Practices
Bibliographic record
Abstract
Coordinated access and coordinated entry systems have become central features in community responses to homelessness in Canada and the United States. Coordinated systems assess individuals and families experiencing homelessness on their needs, prioritize them based upon these needs, and then match them to appropriate housing. Despite the widespread implementation of coordinated systems, there have been few evaluations of the effectiveness of these systems. The current article fills this knowledge gap by providing an overview of the evidence and a critical commentary on the four pillars of coordinated systems—(a) access, (b) assessment, (c) prioritization, and (d) matching and referral—and presenting a critique of current practices. Using the policy streams framework, the critique demonstrates that the components of coordinated systems lack a strong evidence base and that there is little evidence that coordinated systems improve individual-level outcomes such as length of stay in housing. Further, current coordinated system practices, particularly assessments, may be contributing to inequitable access to housing. Limitations of the critique and considerations for implementation are discussed.
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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 | 0.160 | 0.440 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.018 | 0.059 |
| Scholarly communication | 0.019 | 0.025 |
| Open science | 0.018 | 0.011 |
| Research integrity | 0.073 | 0.102 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".