Co-locating Older Retirement Home Residents: Uncovering an Under-Researched Population via Postal Code
Bibliographic record
Abstract
BACKGROUND: Retirement home residents represent a growing proportion of older Ontarians who cannot be identified within existing administrative databases. OBJECTIVE: This article aims to develop an approach for determining, from an individual's postal code, their likelihood of residing in a retirement home. METHODS: We identified 748 licensed retirement homes in Ontario as of June 1, 2018, from a public registry. We developed a two-step evaluation and verification process to determine the probability (certain, likely or unlikely) of identifying a retirement home, as opposed to other dwellings, within a postal code. RESULTS: We identified 274 (36.7%) retirement homes within a postal code certain to indicate that a person was residing in a retirement home, 200 (26.7%) for which it was likely and 274 (36.7%) for which it was unlikely. Postal codes that were certain and likely identified retirement homes with a capacity for 59,920 residents (79.9% of total provincial retirement home capacity). CONCLUSION: It is feasible to identify a substantive cohort of retirement home residents using postal code data in settings where street address is unavailable for linkage to administrative databases.
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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 | 0.001 | 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 | 0.000 |
| 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".