Bibliographic record
Abstract
An increasing number of senior tenants who are aging in place face isolation and barriers which impact their ability to manage their lives. The purpose of this study is to examine the characteristics and issues faced by racialized seniors who currently reside in Toronto Community Housing (TCHC) buildings. These issues include, but are not limited to, isolation, poverty, food security, hoarding, and health. This research analyzes three of TCHC’S core programs that are implemented to resolve tenants’ issues and support tenants in achieving and maintaining successful tenancy: (1) Referral agreements; (2) Buried Treasures and (3) Eviction prevention. This study draws on current literature including how current government policies impact and act as barriers to resolving issues faced by seniors. This study identifies the impact of the core programs in respect to the seniors who live in TCHC, which includes factors, such as, socializing amongst other senior tenants, improving relationships with caregivers, and the overall improvement of units. It is evident that housing affects the physical and mental health of families and individuals. The paper engages these findings with an analysis of TCHC’s policies and documents designed to address ageing in the city of Toronto – focus on senior ageing in TCHC housing units. The City of’ Toronto’s Seniors Strategy (2013) (and 2.0) discusses some discrepancies between intersectional identifiers and quality social care. The methodology used to extract the findings included quantitatively and qualitative data collection and analytical assessment of governing policies. The tools were consist of questionnaires and focus group studies. The tools were administered to TCHC seniors, TCHC staff, vendors and external staff who provide contractual work. The findings suggest that there are significant strengths and considerations that the research subjects’ harness and utilized in the navigation of applicable specialized social services. It also highlights the importance of intersections considerations such as, age, gender, race, and income and immigration status in regard to the implementation of policies, programming and adequate care. The paper also points to directions for future research to better understand the ageing process from a critical, intersectional perspective and locate it in a current discussion of urban theory.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".