Assessing the impact of Connect 2 Care on the residential stability of homeless and vulnerably housed clients
Bibliographic record
Abstract
Background Certain kinds of housing instability, such as foreclosure and homelessness, have been associated with poorer physical and mental health. The Connect 2 Care (C2C) program targets medically complex individuals who are unstably housed, primarily aimed at reducing acute care utilization and connecting clients to appropriate community-based care. However, because housing status is a fundamental determinant of health, the team also assists clients in finding permanent housing. As the C2C program aims to improve the health of its clients, we hope that this intervention positively impacts the housing stability of clients. Objective To determine whether the C2C program is effective in reducing factors of housing instability, such as the frequency of housing moves made, and time spent in unstable housing (such as shelters or sleeping outside). Methods C2C clients were asked to participate in 6- and 12- month follow-up surveys with a member of the research team. During both surveys, participants were prompted to describe their housing history using the Residential Time-Line Follow-Back (rTLFB) inventory. Starting at six months prior to their intake into C2C, participants created a twelve- to eighteen-month timeline that detailed their residential locations and number of housing transitions. Location descriptions provided by clients were categorized as stable, temporary, institutional, or literal homelessness. The number of housing transitions and the proportion of time spent in each housing category were then calculated for each individual. Changes in proportion of time spent over three unique time periods were evaluated using Wilcoxon’s paired rank test with Holm’s multiplicity correction. Results Since September 2018, housing data was collected from 100 unique clients. In comparing the six months preceding C2C intake with the six-to-twelve months after C2C intake, significant reductions in the amount of time spent in literal homelessness (p < 0.001) and reductions in the number of housing changes (p = 0.014) were observed. Discussion Housing stability for C2C clients improved after enrolment in the program. This study was potentially limited by incomplete sampling of the C2C population. Based on our findings, further research should be conducted in evaluating the relationship between increases in housing stability and increases of health status. Acknowledgements The C2C research team thanks Alberta Innovates and the Canadian Institute of Health Research for their financial support. The authors have no conflict of interests to state.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.000 |
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".