Skip to main content

Association of auditory processing abilities and employment in young women



This study aimed to investigate the relationship between employment status and auditory processing abilities in women, considering the cognitive impact of work-related demands. The objective was to study the influence of employment on forward -, backward -, operation span, 2n back, and speech perception in noise (SPIN) scores.


Fifty-eight women aged 20 to 64 years were categorized into working and non-working groups. Auditory cognitive assessments included forward and backward digit span, auditory 2n-back, operational span, and speech perception in noise. Mental workload, listening effort, and fatigue levels were evaluated for each test using standardized scales. Handedness was assessed using the Edinburgh Handedness Inventory. Statistical analysis involved Mann–Whitney tests and effect size calculations.


Working women scored significantly higher in 2n-back task compared to non-working counterparts, emphasizing the positive impact of employment on working memory and cognitive control processes. The working women group perceptually rated greater mental load on the operational span task than non-working group, indicating the task-specific manifestation of group differences. When the tasks are easier (2n back) or difficult (operation span), employed women exhibited an advantage in auditory processing over their counterparts.  


This study reveals nuanced cognitive differences influenced by employment status in women. Working women demonstrated superior auditory processing abilities, specifically in working memory tasks, suggesting potential cognitive benefits associated with engagement in the workforce. The elevated mental workload for dual task working memory paradigms underscores the complexity of simultaneous processing and storage, highlighting cognitive challenges in specific occupational contexts.


The cognitive abilities of adults are closely linked to the job specific demands and skills [25], with gender-based influences. Women constitute half of the global population and play multiple roles in society’s development. They contribute significantly to both nation-building and improving the quality of life for their families. Women in adulthood often strive to find a balance between biological changes, work-related issues, family responsibilities, financial security, and personal goals.

According to several studies [7, 15, 33, 37], employment status has been found to have a significant impact on cognitive functioning throughout life. Unemployed individuals tend to experience higher levels of mental health issues such as stress, anxiety, and depression [16]. They are also more prone to developing chronic diseases like hypertension, cardiovascular disease, diabetes, and musculoskeletal disorders, leading to poor mortality rates [32]. Unemployment often results in inadequate savings or income, creating difficulties in accessing optimal healthcare services in the long term [10]. In addition to these health concerns associated with unemployment, the absence of paid employment is also linked to notable cognitive effects.

Numerous studies have indicated that individuals engaged in intellectually challenging and complex occupations tend to maintain better cognitive abilities as they age, in comparison to those who do not have such occupations [7, 15, 33, 37]. Occupations characterized by cognitive complexity have been associated with a lower prevalence of cognitive decline [6], and it has been proposed that occupational demands contribute to enhancing cognitive reserves [6], with no exception to women. Specific to women, aging and employment have been found to alter the ability to process speech in noisy environments. Women may experience changes in speech perception as they age [28, 35].

In continuation with the other systems, where a strong relationship between employment and gender exists, changes in auditory processing can also be hypothesized. Several factors have been identified as influencing auditory processing and cognition, including employment status [5, 13]. Given the significance of employment and work-related demands, it is concerning that the lack of employment-related mental activities may pose a higher cognitive risk to non-working women, potentially affecting their auditory processing abilities [17, 26, 27, 29]. While there is ample evidence indicating gender disparities in employment and the role of employment in cognitive health, there is currently insufficient scientific consensus on the specific impact of unemployment status on auditory processing abilities in women. The aim of the study was to examine the relationship between employment and auditory processing abilities in women. Evidence derived from the present study is crucial to inform appropriate policy measures and ensure cognitive well-being among women.



A total of 58 female participants, within the age range of 20 to 64 years (mean age = 31.27 ± 9.76 years) took part in the study. The participants were further divided into two groups: working women and non-working women. The working group consisted of 33 participants (mean age = 31.93 ± 8.95 years), whereas the other had 25 participants (mean age = 30.40 ± 10.87 years). In selecting participants for the study, we prioritized individuals whose work primarily involved mental activity over those whose work involved primarily physical activity. The women working in sectors where mental activity is the main focus involved fields such as human resources development, schooling and education, healthcare, administration, information technology, finance, legal professions, engineering, management, public relations and journalism.

Inclusion criteria

To be included in the study, participants in both groups had to meet the following criteria:

  1. a.

    Normal hearing sensitivity, confirmed using an online AIISH hearing screening app (AIISH Hearing Screening App—Apps on Google Play,) [1] for a quick and convenient assessment of hearing abilities without the need for specialized equipment. Using the app facilitated hearing screening even in the remotest areas where data collection was done and where no equipment was available.

  2. b.

    Normal functioning of the middle ear, determined through a detailed case history and screening tympanometry test.

  3. c.

    No history of speech, language, otological, or neurological problems was ascertained through thorough case history and medical reports provided by the individuals.

  4. d.

    No reported history of significant noise exposure.

  5. e.

    Right-handed individuals, as determined by the Edinburgh Handedness Inventory by Oldfield [30]

  6. f.

    Normal cognitive abilities were screened using the Neurobehavioral Evaluation System Test [12].

  7. g.

    Proficiency in Hindi and English either as a primary or secondary language acquired during formal education, as they are one of the official languages of India.


Cognitive allocation was measured through assessments of mental load and working memory (WM) tasks, including forward span, backward span, operation span, and 2n-back, using the Smriti Sharavan 3.0 Software [39]. Speech perception in noise was assessed using SPIN-Indian English [38]. While  listening fatigue levels and effort were evaluated using the Fatigue Assessing Scale [23] and Effort Assessment Scale [18], task specific mental load was measured with the NASA Task Load Index (TLX) [11]. Other than these tests, handedness was also obtained using the Edinburgh Handedness Inventory [30]. Through this inventory, details were gathered regarding individuals’ preferences for specific activities, including writing, drawing, and using a spoon. Only individuals identified as right-handed were included in the selection process.

The cognitive abilities were screened using the Neuropsychological Evaluation Screening Tool (NEST) [12], a quick cognitive screening tool which covers evaluation of attention, memory, perception, verbal and language skills, constructional ability, and executive functioning. The optimum cutoff for NEST is ≥  3 for categorizing as cognitive impairment.

All the tests were administered in a quiet room with noise level below 35 dB(A). In order to monitor the environmental noise, an android-based application Sound Meter developed by Smart Tools Company [19] was used at the participants’ end.

Cognitive assessment

  • Forward and backward digit spans: In the forward digit span test, participants listened to a series of random numbers (1–9) through headphones with a 1000 ms gap between each number. The test ranged from simple (2 digits) to complex (9 digits), with three practice rounds. Participants had to repeat the digits in the same order within 5000 ms. The backward span test was similar but required participants to type the digits in reverse order. Scores were based on the maximum correct digits repeated in the correct or reverse order, displayed by the software.

  • Auditory 2n back: The auditory 2n-back task required participants to repeat the second-to-last number heard in a series via headphones. There were 15 trials, each with a 1000 ms interval, a 5000 ms time limit, and varying string lengths from 4 to 10 numbers. Scoring depended on correct responses by participants.

  • Operation span: In the operation span task, the participant’s ability to remember the target stimuli was assessed. The stimulus was presented along with a secondary task involving solving an arithmetic problem, followed by recalling a bi-syllabic English target word.

  • Speech perception in noise—Indian–English (SPIN-IE): Speech perception in noise (SPIN-IE) involved phonemically balanced words spoken in Indian–English, presented alongside background noise generated by eight Indian–English speakers at a signal-to-noise ratio of 0 dB [38]. The test consisted of 25 carefully selected words presented in the presence of 8 talker speech babble noise with strategically positioned interruptions to avoid overlap with word stimuli.

Mental workload, listening effort and fatigue levels assessment

The mental workload for each WM and SPIN task was assessed using the NASA TLX [11] across six domains: mental demand, physical demand, temporal demand, effort, performance, and frustration level. Participants rated their workload using a visual analog scale ranging from 0 to 10 where 0 indicates less load and 10 indicates maximum load. The factor weights corresponding to each domain were added to the ratings. Thus, obtained weighted average for each domain was obtained by dividing the sum of adjusted ratings by 15. The difference in mental load between the two groups was compared based on the weighted rating. The higher the score, the greater the mental load.

Listening effort and fatigue were assessed at the end of all the tests for each participant. Assessing these two perceptual ratings, in addition to mental load, helps to screen out challenges in extended periods of auditory stimulation in everyday listening environments. Listening effort was evaluated utilizing the Effort Assessment Scale (EAS), a six-question tool developed by Alhanbali et al. [4]. This scale incorporates the three effort-related inquiries from the Speech, Spatial Quality (SSQ) Hearing Scale [18], such as “How much do you have to concentrate when listening to someone?” rated on a 10-point visual analog scale where 0 signifies no effort and 10 indicates a lot of effort.

Fatigue level was appraised using the 10-question Fatigue Assessment Scale (FAS) [23]. The FAS serves as a standardized generic scale for fatigue, demonstrating commendable internal consistency, reliability, and validity [23]. Participants were prompted to assess their daily fatigue by responding to statements like “I get tired very quickly” on a 5-point Likert scale ranging from never to always.

Statistical analysis

The collected data were analyzed using the statistical package for Social Sciences (SPSS) version 25.0 (IBM Corp., Armonk, NY, USA). The Shapiro–Wilk test was administered to assess the normality of data. Mann–Whitney test was performed to identify group differences in WM scores, SPIN Scores, NASA task force ratings, Fatigue ratings, and listening effort ratings. Whenever there was a statistically significant difference between the groups, the effect size was calculated based on the Rosenthal formula re = /Z// √N [34].


Comparison of the working memory and SPIN scores between the two groups

Based on the Shapiro–Wilk test results, the data in both working and non-working groups deviates from a normal distribution (p < 0.05). The median scores of working group were comparatively higher than the non-working group on all WM tasks except operational span and SPIN-IE, as shown in Fig. 1. However, only 2n back withstood statistical testing. Mann–Whitney U tests confirmed that the 2n-back scores of working women were significantly greater than the non-working group (/Z/ = 3.78, p < 0.001). However, the performance of the non-working group was similar to the working group on all other tests including the forward span (/Z/ = 1.88, p > 0.05), backward span test (/Z/ = 1.12, p > 0.05), operational span task (/Z/ = 0.06, p > 0.05), and SPIN test (/Z/ = 0.59, p > 0.05) compared to the non-working group.

Fig. 1
figure 1

Comparison of performance of the two groups for forward digit span (A), backward digit span (B), operation span (C), 2n-back scores (D), and SPIN-IE (E). Individual data points along with box plots (median, interquartile range—IQR) are given representing the scatteredness of data

Comparison of the perceptual ratings (mental load, listening effort, and fatigue) scores between the two groups

Similarly, the results of the Mann–Whitney U test found no statistically significant group differences in the NASA task force scores (p > 0.05) across the tests (forward span: /Z/ = 0.53, p > 0.05; backward span test: /Z/ = 1.14, p > 0.05; 2n back: /Z/ = 1.65, p > 0.05, and SPIN test: /Z/ = 1.38, p > 0.05), except for operation span task wherein the working group women rated (NASA task force scores) the items of operational span test to have significantly greater mental load (/Z/ = 3.44, p = 0.001, re = 0.45) compared to the non-working group, as shown in Table 1. Also, on comparison of within group scores, the median ratings of the 2n back was lower than the other tests, while the highest mental load was on the Operation span tasks in both the groups. The results of the Mann–Whitney U test found no statistically significant group differences in the overall scores of the fatigue scale (/Z/ = 1.45, p > 0.05) and listening effort scale (/Z/ = 0.71, p > 0.05).


The results of the present study highlight the relationship between employment status and various cognitive tasks and SPIN. Though not statistically significant, the results of descriptive analyses showed that working women exhibited better WM scores (Fig. 1) compared to non-working group. This finding aligns with the broader literature emphasizing the positive impact of intellectually challenging occupations on cognitive abilities [7, 15]. Madhavan et al. [22] found that middle-aged women who are employed demonstrate better cognitive abilities, suggesting a positive impact of employment on cognitive functioning. This is supported by Miller et al. [24], who highlighted the psychological impact of job conditions on cognitive functioning in women.

Similar to the advantages enjoyed by working women in terms of WM, women who were employed also had higher SPIN scores (Fig. 1) compared to those who did not have employment. Research has shown a complex relationship between employment status and auditory processing abilities in women. Akeroyd [2] found that cognitive abilities, particularly working memory, can influence speech recognition in noise. Lunner [20] further supported this finding by demonstrating that high cognitive performance is associated with better speech recognition, even with hearing aids. Beaman [9] highlighted the impact of auditory distraction on cognitive performance in the workplace, suggesting that employment status may influence auditory processing abilities. Lastly, [21] demonstrated that working memory capacity, which can be influenced by employment demands, is related to speech understanding in noisy environments.

On Inferential statistics using the Mann–Whitney U test, the superiority of working women in the auditory 2n-back task over their non-working counterparts was established. The 2n-back task, a measure of working memory and cognitive control processes [31], demonstrated statistically significant differences between working and non-working women, with the former group exhibiting significantly superior performance (Fig. 1). The use of 2n-back in the study might be sensitive for employment induced changes in auditory WM. In an experiment, Alain et al. [3], focused on auditory spatial working memory using an n-back task design. The results of their study showed that the hit rate was lower and response time was slower in the 2-back condition compared to the 1-back condition. While false alarms were slightly higher in the 2-back condition, the difference was not statistically significant.

The study found no differences in EAS and FAS scores among groups, indicating that listening effort and fatigue did not impose limitations on auditory processing tasks during extended periods. This screening effectively ruled out any effects of fatigue or effort on task performance over time. The significantly enhanced performance in the 2n-back task suggests that engagement in the workforce may specifically influence working memory and cognitive control processes, contributing to the maintenance of these cognitive functions among employed women. Working memory, a crucial component of cognitive functioning, involves the temporary storage and manipulation of information for ongoing tasks [8]. The 2n-back task, requiring participants to recall the second-to-last item in a sequence, taps into both short-term memory storage and the ability to update information in real-time working memory [31]. On analysis of the NASA TLX mental effort (Table 1), the median scores of 2n-back in both working and non-working women groups were found to be the lowest compared to other tasks. In contrast, the operational span task, a measure of complex WM  assessed in the study, showed the highest mental workload scores (Table 1). This indicates that participants perceived the operational span task as the most demanding among the cognitive assessments. The operational span task involves simultaneous performance of an arithmetic problem while remembering a target stimulus, reflecting the dual-task nature of WM [36]. The higher mental workload scores in both groups for the operational span task are consistent with the literature on WM tasks, indicating that tasks requiring simultaneous processing and storage impose greater cognitive demands [14]. Also, the perceptual ratings of mental load of the working group were significantly higher in working group than non working group. By coagulating the findings that working women had the advantage in both 2n-back scores (Fig. 1) and perceptual mental load (NASA TLX), it can be understood that when the perceptual difficulty of the tasks is either easy or difficult, the benefits derived from cognitive demands of work are manifested. In addition, the emergence of group differences is also regulated by the task complexity. When the tasks involve a distracter or a dual task paradigm, the groups differences are readily observable. In case of operation span, the cognitive load of dual tasks may be particularly taxing. As observed, the mental workload scores were high (Table 1), as the cognitive effort required to simultaneously engage in arithmetic operations and maintain target stimuli in memory. However in 2n-back task, ignoring the last-but-two numeral was easy on mental load. Despite the mental load aspect, due to its inherent nature of real-time updating of memory process, 2n back was sensitive to employment induced changes in WM.

Table 1 Median, interquartile range, /Z/, and p value for the mental load ratings from the NASA TLX for each test performed on the participants


In conclusion, the study provides valuable insights into the specific cognitive domains influenced by employment status in women. The superior performance on the 2n-back task suggests that working women may experience cognitive benefits, particularly in working memory and cognitive control processes. Moreover, the elevated mental workload scores for the operational span task emphasize the intricate nature of dual task working memory paradigms, shedding light on the cognitive challenges associated with simultaneous processing and storage.

Availability of data and materials

No data are available.


  1. AIISH Hearing Screening App—Apps on Google Play. Retrieved February 19, 2024, from

  2. Akeroyd MA (2008) Are individual differences in speech reception related to individual differences in cognitive ability? A survey of twenty experimental studies with normal and hearing-impaired adults. Int J Audiol 47(sup2):S53–S71.

    Article  PubMed  Google Scholar 

  3. Alain C, Shen D, Yu H, & Grady C. (2010). Dissociable memory- and response-related activity in parietal cortex during auditory spatial working memory. Front Psychol 1:202.

  4. Alhanbali S, Dawes P, Lloyd S, Munro KJ (2017) Self-reported listening-related effort and fatigue in hearing-impaired adults. Ear Hear 38(1):e39–e48.

    Article  PubMed  Google Scholar 

  5. Alley D, Suthers K, Crimmins E (2007) Education and cognitive decline in older Americans: results from the AHEAD sample. Res Aging 29(1):73–94.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Andel R, Finkel D, Pedersen NL (2016) Effects of preretirement work complexity and postretirement leisure activity on cognitive aging. J Gerontol B Psychol Sci Soc Sci 71(5):849–56.

    Article  PubMed  Google Scholar 

  7. Andel R, Infurna FJ, Hahn Rickenbach EA, Crowe M, Marchiondo L, Fisher GG (2015) Job strain and trajectories of change in episodic memory before and after retirement: results from the Health and Retirement Study. J Epidemiol Community Health 69(5):442–446.

    Article  PubMed  Google Scholar 

  8. Baddeley A (2003) Working memory: looking back and looking forward. Nat Rev Neurosci 4(10):829–839.

    Article  CAS  PubMed  Google Scholar 

  9. Beaman CP (2005) Auditory distraction from low-intensity noise: a review of the consequences for learning and workplace environments. Appl Cogn Psychol 19(8):1041–1064.

    Article  Google Scholar 

  10. Bell DNF, Blanchflower DG (2019) The well-being of the overemployed and the underemployed and the rise in depression in the UK. J Econ Behav Organ 161:180–96.

  11. Cao A, Chintamani KK, Pandya AK, Ellis RD (2009) NASA TLX: software for assessing subjective mental workload. Behav Res Methods 41(1):113–117.

    Article  PubMed  Google Scholar 

  12. Chopra S, Kaur H, Pandey RM, Nehra A (2018) Development of neuropsychological evaluation screening tool: an education-free cognitive screening instrument. Neurol India 66(2):391–399.

    Article  PubMed  Google Scholar 

  13. Christensen H, Korten AE, Jorm AF, Henderson AS, Jacomb PA, Rodgers B, Mackinnon AJ (1997) Education and decline in cognitive performance: compensatory but not protective. Int J Geriatr Psychiatry 12(3):323–330.;2-n

    Article  CAS  PubMed  Google Scholar 

  14. Conway ARA, Kane MJ, Engle RW (2003) Working memory capacity and its relation to general intelligence. Trends Cogn Sci 7(12):547–552.

    Article  PubMed  Google Scholar 

  15. Correa Ribeiro PC, Lopes CS, Lourenço RA (2013) Complexity of lifetime occupation and cognitive performance in old age. Occupat Med (Oxford, England) 63(8):556–562.

    Article  CAS  Google Scholar 

  16. Dean JA, & Wilson K. (2009). “Education? It is irrelevant to my job now. It makes me very depressed. exploring the health impacts of under/unemployment among highly skilled recent immigrants in Canada. Ethnicity 14(2)185-204

  17. Füllgrabe C, Moore BCJ, Stone MA (2014) Age-group differences in speech identification despite matched audiometrically normal hearing: contributions from auditory temporal processing and cognition. Front Aging Neurosci 6:347.

    Article  PubMed  Google Scholar 

  18. Gatehouse S, Noble W (2004) The speech, spatial and qualities of hearing scale (SSQ). Int J Audiol 43(2):85–99.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Ibekwe TS, Folorunsho DO, Dahilo EA, Gbujie IO, Nwegbu MM, Nwaorgu OG (2016) Evaluation of mobile smartphones app as a screening tool for environmental noise monitoring. J Occup Environ Hyg 13(2):D31-36.

    Article  PubMed  Google Scholar 

  20. Lunner T (2003) Cognitive function in relation to hearing aid use. Int J Audiol 42(Suppl 1):S49-58.

    Article  PubMed  Google Scholar 

  21. Lyxell B, Andersson U, Borg E, Ohlsson I-S (2003) Working-memory capacity and phonological processing in deafened adults and individuals with a severe hearing impairment. Int J Audiol 42(sup1):86–89.

    Article  Google Scholar 

  22. Madhavan A, Bajaj G, Dasson Bajaj P, D’Souza DF (2022) Cognitive abilities among employed and unemployed middle-aged women – a systematic review. Clin Epidemiol Glob Health 15:101042.

    Article  CAS  Google Scholar 

  23. Michielsen HJ, De Vries J, Van Heck GL, Van de Vijver FJR, Sijtsma K (2004) Examination of the dimensionality of fatigue: the construction of the Fatigue Assessment Scale (FAS). Eur J Psychol Assess 20(1):39–48.

    Article  Google Scholar 

  24. Miller J, Schooler C, Kohn ML, Miller KA (1979) Women and work: the psychological effects of occupational conditions. Am J Sociol 85(1):66–94.

    Article  Google Scholar 

  25. Moen P, & Wethington, E. (1999). Midlife development in a life course context. 3–23.

  26. Mukari SZMS, Yusof Y, Ishak WS, Maamor N, Chellapan K, Dzulkifli MA (2020) Relative contributions of auditory and cognitive functions on speech recognition in quiet and in noise among older adults. Braz J Otorhinolaryngol 86(2):149–156.

    Article  PubMed  Google Scholar 

  27. Murphy CFB, Rabelo CM, Silagi ML, Mansur LL, Bamiou DE, Schochat E (2018) Auditory processing performance of the middle-aged and elderly: auditory or cognitive decline? J Am Acad Audiol 29(1):5–14.

    Article  PubMed  Google Scholar 

  28. Musiek FE, Shinn J, Chermak GD, Bamiou DE (2017) Perspectives on the pure-tone audiogram. J Am Acad Audiol 28(7):655–671.

    Article  PubMed  Google Scholar 

  29. Obuchi C, Ogane S, Sato Y, Kaga K (2017) Auditory symptoms and psychological characteristics in adults with auditory processing disorders. J Otology 12(3):132–137.

    Article  Google Scholar 

  30. Oldfield RC (1971) The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia 9(1):97–113.

    Article  CAS  PubMed  Google Scholar 

  31. Owen AM, McMillan KM, Laird AR, Bullmore E (2005) N-back working memory paradigm: a meta-analysis of normative functional neuroimaging studies. Hum Brain Mapp 25(1):46–59.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Paul KI, Geithner E, Moser K (2009) Latent deprivation among people who are employed, unemployed, or out of the labor force. J Psychol 143(5):477–491.

    Article  PubMed  Google Scholar 

  33. Ribeiro PCC, Lourenço RA (2015) Lifetime occupation and late-life cognitive performance among women. Health Care Women Int 36(12):1346–1356.

    Article  PubMed  Google Scholar 

  34. Rosenthal R, Rubin D (2003) r equivalent: a simple effect size indicator. Psychol Methods 8:492–496.

    Article  PubMed  Google Scholar 

  35. Schneider B, Pichora-Fuller K, Daneman M (2009) Effects of senescent changes in audition and cognition on spoken language comprehension. The Aging Auditory System. pp 167–210.

    Chapter  Google Scholar 

  36. Turner ML, Engle RW (1989) Is working memory capacity task dependent? J Mem Lang 28(2):127–154.

    Article  Google Scholar 

  37. Vemuri P, Lesnick TG, Przybelski SA, Machulda M, Knopman DS, Mielke MM, Roberts RO, Geda YE, Rocca WA, Petersen RC, Jack CR (2014) Association of lifetime intellectual enrichment with cognitive decline in the older population. JAMA Neurol 71(8):1017–1024.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Yathiraj A, Vanaja CS, Muthuselvi T (2010) Maturation of auditory processes in children aged 6 to 10 years. A Dep Proj Submitt to All India Inst Speech Hear; Mysore, India. Retrived from

  39. Kumar A, Maruthy S. Development and Test Trail of Computer Based Auditory-Cognitive Training Module for Individuals with Cochlear Hearing Loss. A Dep Proj Submitt to All India Inst Speech Hear. Retrived from

Download references


The investigators express their appreciation to the Director of the All India Institute of Speech and Hearing and the Head of the Department of Audiology for providing the necessary resources for testing. Sincere thanks are extended to all the participants who cooperated in this study. The researchers are grateful for their kind cooperation. It is important to note that the study did not receive any external funding.


This research was not funded.

Author information

Authors and Affiliations



YS contributed to the design, data collection, and processing, analyzing the data, reviewing the literature, and writing. NK supervised, analyzed, and interpreted the data along with writing and critically reviewing. Both authors read and approved the final manuscript.

Corresponding author

Correspondence to Yoshita Sharma.

Ethics declarations

Ethics approval and consent to participate

The study strictly adhered to ethical guidelines established for bio-behavioral research, as outlined by Basavaraj and Venkatesan (2009) with the reference number SH/AUD/2022-23/UR on 22/12/22 by Institutional review board (IRB). Before participating in the study, informed consent was obtained from each participant.

Consent for publication

Not applicable

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sharma, Y., K.V., N. Association of auditory processing abilities and employment in young women. Egypt J Otolaryngol 40, 53 (2024).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: