A critical review of the literature on comfort of hearing protection devices: definition of comfort and identification of its main attributes for earplug types
Bibliographic record
Abstract
Objective: This article presents a comprehensive literature review of past works addressing Hearing Protection Devices (HPD) comfort and to put them into perspective regarding a proposed holistic multidimensional construct of HPD comfort.Design: Literature review.Study samples: Documents were hand searched and Internet searched using “PubMed”, “Web of Science”, “Google Scholar”, “ProQuest Dissertations and Theses Professional”, “Scopus” or “Google” search engines. While comfort constructs and measurement methods are reviewed for both earplugs and earmuff types, results and analyses are provided for the earplug type only.Results: This article proposed a multidimensional construct of HPD comfort based on four dimensions: physical, functional, acoustical and psychological. Seen through the prism of the proposed holistic construct of HPD comfort, the main comfort attributes of earplugs have been identified for each comfort dimension.Conclusions: The observed lack of consensus on the definition of HPD comfort in the scientific community makes it difficult to prioritise the importance of comfort attributes yet necessary for future design of comfortable earplugs.
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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".