Uprooting and Rerooting: A Critical Race Informed Narrative Inquiry of LTC Home Culture with Stories Told by Thamizh (Tamil) Elders
Bibliographic record
Abstract
People of colour (POC) living in long-term care (LTC) homes are affected by systematized difference (including structural racism) every day. Due to differences between the predominant, largely Eurocentric provision of care culture in Canada and Eastern ways of caring, caring in multi-ethnic Canada requires strong leadership and cultural sensitivity for effective elder care. The first step to transform the culture of LTC is to hear stories of residents living in LTC homes, especially those who are marginalised by difference. According to residents’ stories from this study, changes in practice and policy can be put in place for more equitable spaces and comfortable living in LTC homes. The purpose of this study was to hear stories told by Thamizh (commonly referred as ‘Tamil’) elders that speak to the culture of living in LTC homes in Southern Ontario, Canada. Specifically, this critical narrative study, uses considerations from Critical Race Theory (CRT) to expose subtle ways practices in LTC homes marginalise POC and individuals of difference. My hope is that this research moves beyond these pages to contribute to policy changes and informs Ontario’s Ministry of Health & Long-Term Care (MOHLTC) about the ways POC living in Ontario’s LTC homes encounter (systemic and individual) discrimination. I propose that the MOHLTC sharpen the resident bill of rights to guide LTC homes to meet the diverse needs of residents residing in LTC homes. \nTo resist the status-quo reproduction of Euro-dominant practices, LTC home living must be reshaped to include: (1) diverse programs such as cultural special events, (2) connections with local spiritual organizations, (3) partnerships with meal delivery services to offer traditional food, and (4) the facilitation of diverse social groups for residents to foster meaningful relationships with others in the home. Through changes in policy the normalized processes of racialization can be confronted and diversity can be honoured in LTC homes.
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 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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.033 | 0.042 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".