The response to noise distraction by different personality types : An extended psychoacoustics study
Bibliographic record
Abstract
Noise distraction remains a leading complaint among office workers worldwide. The purpose of this paper is to identify personal factors, including personality traits, that underpin noise distraction of office workers in order to mitigate noise and improve wellbeing and performance. Following on from a previous research study, the same survey methodology has been conducted in a further 11 offices and the larger dataset was used to substantiate the original findings. The new data set consists of 2,145 responses across a range of organisations and countries in real-world settings. The survey consists of eight sections of questions including The Big 5, or OCEAN, personality profile assessment along with ratings of noise and distraction. Nearly 50 per cent of the respondents consider workplace noise to adversely affect their wellbeing and increase stress. As found in the previous study, 67 per cent of respondents rated the effect of noise on performance as negative and a mean estimated impact on work performance was -6 per cent. Contrary to the original study, no statistically significant differences in noise performance between introverts and extroverts were initially revealed. Significant results were, however, found once those in private offices were excluded, such that introverts are more negatively affected by noise when in open plan offices. Noise ratings differed significantly depending on job role, with data processors, analysts and researchers performing less well than the other job roles combined. Furthermore, data processors and analysts were found to be significantly more introverted than other job roles, compounding their response to noise. The research revealed key variables often overlooked in reducing noise distraction in the workplace. Further investigation is required of this multi-layered, complex subject; however, the research demonstrates the need to account for several key personal variables fundamental to designing good acoustic working environments that support wellbeing and performance.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".