Better than Nothing? A scoping review on tiny homes and sleeping cabins as a response to homelessness in high-income countries
Bibliographic record
Abstract
Tiny homes and sleeping cabins are often touted as a quick and efficient solution to providing shelter or housing for unhoused individuals. No known knowledge syntheses have amalgamated this body of literature, and this is needed to inform future research, practice and policy. To address this gap, we conducted a scoping review using the framework advanced by Arksey and O’Malley. We searched seven databases supplemented by a search of grey literature. We included studies that were empirical, non-empirical, of any design, and both peer-reviewed and non-peer-reviewed literature. This search yielded 116 unique articles following the removal of duplicates. Over half of the included articles were empirical ( n = 65; 56 %) and less than half were peer-reviewed ( n = 43; 37.1 %). The majority of articles represented literature pertaining to the United States ( n = 89; 76.7) and Canada ( n = 14; 12.1 %). We generated four themes in our narrative synthesis: 1) Tiny homes and sleeping cabins as a viable component of a broader solution to homelessness; 2) How to make it happen; 3) Critical perspectives on the use of tiny homes and sleeping cabins as a response to homelessness; and 4) It’s better than nothing. We conclude that a range of articles on this topic have been published, yet this literature remains under-developed with few empirical studies providing evidence for the effectiveness of tiny homes and sleeping cabins over other interventions. More empirical research is needed to warrant the use of tiny homes and sleeping cabins as a response to homelessness in light of literature on alternative approaches.
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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 | 0.025 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".