235 Revitalizing Tenant Wellness: Piloting the Implementation of an In-Building Primary Care Wellness Hub at 100 High Park Avenue for Greater Access to Long-Term Mental Health Services for Underserved Tenants
Bibliographic record
Abstract
OBJECTIVES/GOALS: In a collaborative effort with Toronto Community Housing (TCH), West Toronto Ontario Health Team (WTOHT), and our translational research team, we seek to increase the availability and long-term use of mental health care services through an in-building Wellness Hub for underserved tenants residing in a medical desert, 100 High Park Avenue. METHODS/STUDY POPULATION: In our pilot study, we will use an evaluative framework based on resident feedback to gauge the effectiveness of wellness hub implementation. We will examine both survey results, information from key stakeholder conversation and final interview data from the resident participants. Post wellness hub implementation we will inquire about various aspects of their experience in residence related to the main pillars of our project, including the presence of key wellness resources, accessibility of resources, effectiveness of those resources. Qualitative data management softwares results from the semi-structured interview will be coded and analyzed to extract themes and relevant changes throughout and after intervention implementation. RESULTS/ANTICIPATED RESULTS: The anticipated project results would be based on the following research question findings: How might we develop and integrate accessible mental health services through the wellness hubs for tenants at 100 High Park Avenue to improve the long-term follow-through of their care? The primary outcome of this project would be collective improvement in mental health of tenants at TCH 100 High Park Avenue. Qualitative evidence in the form of semi-structured interviews of tenants at baseline and after wellness hub implementation are expected to indicate an improvement in their mental health. Secondary outcomes for tenants include fewer incidences of feeling unsafe, reduced drug dependency, and improved community cohesion. DISCUSSION/SIGNIFICANCE: A wellness hub will foster well-being and resilience among residents, ultimately enhancing their overall quality of life and community cohesion. Furthermore, the wellness hub model could be scaled across 2,100 TCHC buildings and other metropolitan cities facing similar crisis i.e. New York.
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 | 0.006 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".