The Small House Model to Support Older Adults in Long-Term Care
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it