Identifying Asbestos-Containing Materials in Homes: Design and Development of the ACM Check Mobile Phone App
Bibliographic record
Abstract
BACKGROUND: Asbestos-containing materials (ACMs) can still be found in many homes in Australia and other countries. ACMs present a health risk when they are damaged or disturbed, such as during do-it-yourself home renovations. However, community members lack knowledge and awareness about asbestos identification and its safe management in residential settings. OBJECTIVE: The objective of our study was to describe the process of developing a mobile phone app, ACM Check, that incorporates a questionnaire designed to identify and assess ACMs located in residential settings. METHODS: A multidisciplinary team was involved in the formative development and creation of the mobile phone app. The formative development process comprised 6 steps: defining the scope of the app; conducting a comprehensive desktop review by searching online literature databases, as well as a wider online search for gray literature; drafting and revising the content, questionnaire, conditional branching rules, and scoring algorithms; obtaining expert input; manually pretesting the questionnaire; and formulating a final content document to be provided to the software development company. We then constructed ACM Check on the iOS platform for use in a validation study, and then updated the app, replicated it on Android, and released it to the public. RESULTS: The ACM Check app identifies potential ACMs, prioritizes the materials based on their condition and likelihood of disturbance, and generates a summary report for each house assessed. CONCLUSIONS: ACM Check is an initiative to raise community members' awareness of asbestos in the residential environment and also serves as a data collection tool for epidemiologic research. It can potentially be modified for implementation in other countries or used as the basis for the assessment of other occupational or environmental hazards.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".