Using administrative data from a national shared services database to target the delivery of homeless services in the Dublin Region
Bibliographic record
Abstract
BackgroundPASS is a national shared services database that captures live information on service user interactions with all state funded NGO and local authority homeless services. In the Dublin region, which accommodates in excess of 70% of the nationalhomeless population, this data was mined and cleansed in order to carry out a k-mean cluster analysis. ObjectiveThe objective was to determine the rate of movement through homeless services and the consumption of resources of different clusters cohorts and to compare these findings with other regions internationally. MethodsFollowing extensive data preparation, the Kuhn and Culhane (1998) k-mean cluster analysis was applied in 2017 to five years of PASS data (2012-2016) and results categorised to align to their typology of homelessness. FindingsResults for Dublin showed patterns similar to those reported in the US, Canada and Denmark, with approximately 80% of services users transitioning quickly through services. These transitional service users accounted for just over one third of total bed-nights while the remaining 20% of episodic and long-term service users accounted for almost two thirds of the bed-nights over the five years. Uniquely, the analysis also considered the patterns of engagement of people sleeping rough and results revealed similar but more extreme patterns with 86% of those rough sleeping accounting for only 28% of outreach contacts with a small number adults accounting for over 70% of all street contacts. ConclusionThe results from the analysis of administrative data were used to inform operations so appropriate ‘Housing First’ responses were developed for those episodically or chronically experiencing homelessness and engaged in sleeping rough.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".