Environmental illness prevalence: A population‐based study in Nova Scotia
Bibliographic record
Abstract
OBJECTIVES: Clinic studies demonstrate that people diagnosed with environmental illness experience high levels of disability and health care utilization. Even though the controversial status of this disorder attracts attention, actual prevalence estimates and estimated impact on health care systems are unclear. To address this, we sought both a prevalence estimate and a measure of the degree of health care utilization for those reporting this diagnosis. DESIGN AND METHODS: Point prevalence, as assessed by self-report of professional diagnosis, was established with data from the Nova Scotia Health Survey 1995, a stratified, random sample population survey of 3227 Nova Scotian adults. We compared medical care utilization for the year following the survey, drawn from the provincial medical insurance register, between the 24 cases with no other reported medical conditions and 48 age-, sex-, and education level-matched healthy controls. RESULTS: The adjusted point prevalence of environmental illness was 2.6%. Physician reimbursement costs across the following year were 5.5 times more likely to be above the survey average ($259 CAD) when compared to the healthy control group. CONCLUSIONS: The prevalence of environmental illness diagnoses represents a significant disability and treatment burden, justifying research into case definition and the phenomenology of environmental illness by health psychologists.
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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".