HEALTH<sup>2</sup>: A Holistic Environmental Assessment Lay Tool for Home Health
Bibliographic record
Abstract
Although the adverse health effects of poor indoor air quality on occupants from mold and dampness in indoor environments are well described, there is no reliable empirical tool to evaluate indoor mold and dampness levels in the home for use by the medical profession and health safety regulatory bodies. The economic impact to society approaches $40 billion a year in North America alone from the cost of health care and workplace lost productivity. Mobilizing corrective action necessitates an acceptable home environment evaluation method. This paper proposes a reliable empirical model and tool, the Holistic Environmental Assessment Lay Tool for Home Healthiness, and develops guidelines for its use as a tool to evaluate and rank mold and dampness related indoor environmental conditions associated with known respiratory health outcomes. HEALTH2 was calibrated using theoretical homes and then validated using data from 269 home evaluations where occupant health and the home environment factors were collected. Results suggest the model can be used as an early detection tool to assist in determining indoor environment risk factors associated with respiratory illness from mold and dampness. Empirical modeling and this tool can assist environmental professionals in determining improvement scenarios beyond general industry prescription and assist regulatory bodies in setting home health guidelines. The HEALTH2 model challenges the dominant view and suggests that damp and moldy environments are measurable and the impact to society is sufficient to necessitate prompt medical and regulatory action.
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.
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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".