The Small House Model to Support Older Adults in Long-Term Care
Bibliographic record
Abstract
The small house model of long-term care (LTC) is identified internationally by several model names. Although some differences exist between the characteristics of these models (e.g., number of residents, degree of resident freedom, facility design), there are 3 recurring components: functional units with a small group of residents, replication of familiar domestic routines, and some form of decentralized staff. The key philosophic difference between the small house model and the traditional LTC model is the heavy focus on person-centred care. This approach to care in the small house model is firmly rooted in freedom of choice and autonomy for the residents. Small house models eliminate the strict delineation of roles; staff at all levels are included in the decision-making process. Self-managed and universal work teams are prominent features of the small house model. Frontline staff with strong interpersonal skills are essential for successful implementation. No strong trend emerges from the literature with respect to the impact of the small house model on resident-centred outcomes compared with more traditional models of LTC. This is likely due to lack of consistency in the outcomes that are measured and variability among the different small house models. This finding is consistent with other reviews on the topic. Literature exploring the Canadian experience with small house models is limited. The majority of identified studies used data from the US or European jurisdictions, which potentially limits its generalizability to the Canadian context.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".