Predictors of poor conditions in the home
Bibliographic record
Abstract
Despite popular media portraying hoarding to be a problem of extremely poor housekeeping, most hoarded homes are relatively clean – large amounts of stuff just prevent the home from being functional. Some hoarded homes, however, develop poor living conditions like filth or disrepair. To date, little is known about how homes end up this way. The current study identified unique predictors and generated ideas about complex processes involved in the development of poor living conditions in hoarding. Three community agencies shared in-home assessment data for mainly involuntary clients with problematic living conditions, such as hoarded or filthy homes. These community agencies were the Metropolitan Boston Housing Partnership (n=115) in Boston, MA, the Hoarding Action Response Team (n=137) in Vancouver, BC, and the Hamilton Gatekeepers Program (n=209) in Hamilton, ON. Each site completed in-home assessments from 2010-2014 to evaluate client characteristics (lack of insight, social isolation, state of mind) and conditions of the home (number of pets, clutter accumulation, unusable bathrooms or kitchens) using the HOMES: Multidisciplinary Hoarding Risk Assessment, the Clutter Image Rating Scale, or a similar measure. Site-specific regression analyses identified unique predictors of poor living conditions. Clients with high clutter accumulation were at increased risk for squalor at all three sites, while kitchen or bathroom problems uniquely predicted squalor at two sites. Within two agencies, number of pets was also a consistent predictor of one indicator of squalor, the presence of urine or feces. Few clients had household disrepair (9-12% within sites), but findings hint that disrepair is associated with high clutter accumulation. Findings related to poor insight being a predictor of squalor were mixed. This is the first study to directly examine poor living conditions in hoarding. Replicated study findings across sites suggest that common features of hoarding, such as clutter accumulation and unusable rooms, are unique predictors for squalor. Results from this study can help community agencies that deal with problematic living situations prioritize intervention goals, especially if staff believe clients are at risk for poor living conditions.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".