24. Perspectives of Small-Scale Landlords on Providing Healthy Housing to Populations Living on Low Income: Results of an Ontario-Wide RentSafe Survey.
Bibliographic record
Abstract
Introduction: People living on low income often experience indoor environmental health risks. As team members of the provincially funded RentSafe initiative, our research explores the perspectives of small-scale landlords in Ontario on providing healthy housing to populations living on low income. Our objectives were to: 1) determine the perceived barriers to landlords providing healthy housing, 2) ascertain whether such barriers arise from socioeconomic precariousness among landlords, and 3) mobilize findings within RentSafe to ensure policy and practice responses can better accommodate landlord constraints and priorities. Methods: During February 2017, a survey will be conducted on small-scale landlords living in Ontario. It will be distributed through the email list of the Landlord Self-Help Centre, a legal clinic that serves Ontario landlords. The survey design has been informed by landlord focus groups and a review of literature. Results: Once data collection is complete, we will characterize the demographics of small-scale landlords who seek legal aid and analyze data on their attitudes, knowledge, and behaviour in relation to healthy housing. Furthermore, we will determine if socioeconomic precariousness exists among survey respondents and whether or not this impacts their ability to provide healthy housing. Discussion: This work expands the breadth of RentSafe, and thus heightens its ability to pursue evidence-based strategies to promote healthy housing by bringing in landlord perspectives. Introducing landlord voices into the provincial conversation will shed light on opportunities for public health, government ministries, and other relevant stakeholders to better support small-scale landlords in providing healthy housing to low-income populations.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".