Improving Detection of Work-related Asthma: Validation of the Work-related Asthma Screening Questionnaire (Long-Version)
Bibliographic record
Abstract
Background: The Work-related Asthma Screening Questionnaire (Long-version) (WRASQ(L))TM is designed to screen for work-related asthma (WRA) in primary care and specialist settings. \nPurpose: The purpose of this study was to conduct a definitive evaluation of the WRASQ(L)TM. Another aim was to identify dissemination and implementation strategies for the research findings. \nMethods: This is a multicentre prospective cohort study. Employed adults aged 18-75 years with asthma confirmed by objective measures and the ability to take up to 14 days off work were recruited from two Canadian tertiary care centres. Participants completed the WRASQ(L)TM then completed an objective test. Two specialists blinded to WRASQ(L)TM answers classified test data as WRA or non-WRA. Measurement and diagnostic properties were calculated based on current symptoms and different definitions of a screen positive result. Characteristics between WRA and non-WRA classifications were compared. Strategies for implementation and dissemination were identified via a Project Advisory Committee, questionnaires and two focus groups were analyzed using thematic qualitative analysis. \nResults: Questionnaire sensitivity (SN), specificity (SP), positive and negative predictive a value (PPV and NPV, respectively) were 100%, 14.9%, 19.7%, 100%, respectively. SP increased to 40% while SN and NPV decreased to 85.7% and 92.9%, respectively when only current workplaces were considered. Youden’s index was maximized (J=29.7%) when the cut-off for number of positive answers for a screen positive was ≥3. WRA classifications were significantly younger than non-WRA (p=0.043). Many strategies for implementation were identified. Focus groups provided positive feedback on the WRASQ(L)TM and identified six themes that discuss the barriers, benefits and limitations of eTools in clinical settings: a) involve and address patient needs; (b) novel data collection; (c) knowledge translation; (d) time considerations; (e) functional/practical barriers; and (f) human limitations. \nConclusions: The WRASQ(L)TM has excellent SN and NPV. A larger sample size with increased prevalence of WRA is warranted to clarify the SP and PPV. Narrowing the interpretation of a screen positive to include only current workplaces or increasing the number of positive answers should be considered. Barriers and strategies to implement questionnaire should be noted and considered.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".