Effect of housing condition on quality of life
Bibliographic record
Abstract
This study examined the housing effect on quality of life among Japanese people. In the current cross-sectional study, we analyzed the 1-year of data (November 2015-March 2016) with 2533 participants. We used the Short Form-8 questionnaire, an 8-item instrument that measures general aspects of health-related QOL. Comprehensive Assessment System for Built Environment Efficiency housing checklist which was developed by Ministry of Land, Infrastructure, Transport and Tourism was used to assess the housing aspects. This checklist has six health elements including thermal comfort, acoustic environment, lighting environment, hygiene, safety, and security for 8 distinctive rooms/places of home. Multilevel analysis was done to identify the relationship between the perceived level of housing problem and PCS and MCS by clustering by sex. Compared to those who always felt unsafe at home due to interior design problem, participants who never felt unsafe showed an average of 10.51 (95% CI = 7.69-13.34, p < 0.0001) and 5.78 (95% CI = 2.90-8.65, p < 0.0001) higher physical and mental component score (better quality of life), respectively. Those who never had thermal, acoustic, lighting, hygiene, and security problems of housing also exhibited significantly better quality of life compared to participants who felt these problems.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".