My name's Novia Wang.
In the study, Mental Well-Being at Work: Perspective of Software Engineers,
we interviewed software engineers from different companies in the United States
and explored the current state of software engineers' mental wellbeing at work,
as well as the challenges and potential technologies
that can aid with managing mental wellbeing at work.
I will now highlight some of the study details.
In the United States of America in 2021.
52% of surveyed workers experienced burnout and 76% reported
overall poor mental wellbeing at work, which was an increase from prior years.
This can further exacerbate the impact of poor mental wellbeing.
For example, employee productivity and job performance are affected.
Organizations face problems with job retention,
and workers can even experience physical health symptoms.
To further understand mental wellbeing at work.
The job demand, control, support or the JDCS model can be helpful.
Through this model, we can understand mental wellbeing at work by categorizing
different jobs and employees into different categories,
which help indicate the risk of psychological strain at work.
Based on a low to high spectrum of the three components of the model
that are demands, control and support, different job profiles have been created
by past studies, including low strain,
active, passive and high strain.
Particularly a high strain job profile such as healthcare
typically indicates that there is an increased
likelihood to experience poor mental wellbeing at work.
Other factors such as interpersonal conflicts
with coworkers, individual differences, socio cultural factors,
interruptions and working overtime can also affect workers' mental wellbeing.
Looking at past studies and recent news articles,
we identified some current solutions in which organizations and individuals
have attempted to address mental wellbeing and facilitate its management at work.
These include, but not limited to increased paid time offs,
financial support, employee assistance programs,
flexible work schedules
and wearables for stress sensing using heart rate monitors.
Workers have also used
mental wellbeing mobile applications to manage their mental wellbeing,
and some organizations have offered stipend and financial support,
specifically for wellness related technology purchases.
However, despite the efforts by organizations, employees
continue to feel that these solutions are insufficient and often not useful.
Because of that, we need to explore the lived experience
of employees to better understand the barriers they currently face
and the strategies they currently use to manage their mental wellbeing at work.
Specifically among different professions, software engineering
is one that frequently experiences high job demands and burnout.
Last year, the great resignation occurred because of that.
Additionally, software engineers are reported to have
a high suicide rate compared to other professions
because of their tool-intensive nature and constant changes in their work
requirements, they can also be considered as a high strain
job in the JDCS model.
This study therefore asked three main research questions.
One: How does mental wellbeing manifests itself at work for software engineers?
Two: What challenges and barriers do software engineers face
with current technologies and company policies for mental wellbeing?
Three: What do software engineers expect from technology
for managing mental wellbeing?
We recruited participants through professional networks and snowball
sampling that asked participants to refer others who might be interested.
We then conducted 14 semi-structured
interviews with participants from different companies
with a range of work experience as software engineers.
To analyze the study data, we used interactive open coding,
where two researchers on the team coded all 14 interviews
and then reviewed the results with the rest of the research team.
It is important to note that the research team is interdisciplinary,
consisting of experts from the software engineering field,
human computer interaction and psychological science.
For more details on participants and data analysis,
please refer to our full paper.
We will now highlight some of the study findings.
First, we found that participants relied on a variety
of strategies to manage their mental wellbeing at work.
They balance work and personal time using to do list and microbreaks.
One participant said that they would "watch the clip here
and sit down, maybe read, read some articles".
Some participants prioritize work activities as a coping mechanism.
Feeling like they had no choice but to try and push through it.
Others learned to avoid potential contributing factors
to poor mental well-being, stating that "if I know I go this route,
I'll be stressed in a month, so I don't want to go this route."
And participants found that coping with peers and managers
through shared experiences during challenging time is a helpful strategy.
When working remotely, one participant said that their team and they "basically
just vent to each other while playing Among Us", an online game.
However, we identified
some challenges to successful mental wellbeing management at work.
Particularly, challenges with organizational policies included
overall unawareness of mental wellbeing policie, unfamiliarity with their usage,
and skepticism of the intention of such policies.
One participant stated that their "paranoia was
that the company is paying for the hotline and therefore
how do we know where the reporting is going to?"
Participants also experienced attitudinal challenges related to time
commitment, cost and efficacy of mental wellbeing solutions.
One participant described their experience with mobile applicationsl, "I
just kind of stopped doing it, you know, like, you have so much time in the day.
It's easy for stuff like it to get deprioritized when you're
feeling good." Participants also described technology fatigue as a challenge
to adopting digital mental wellbeing solutions.
Specifically, software engineers frequently work with computers
and technology.
In the words of our participants, even though virtual social support
can aid with mental wellbeing at work, "when I need to relax,
the last thing I want to do is pop on the camera
and do yet another thing on my computer or on my phone."
Participants suggested potential technologies
that they preferred to facilitate mental wellbeing management.
These include automation and passive sensing,
such as analyzing workers' tone of voice and as suggested
by our participants, "using machine learning models that basically can predict
if someone is depressed or even susceptible to suicidal thoughts
by just analyzing their voice," as well as having their computers
remind them to take breaks, "perhaps even block you from doing work.
Maybe at noon.
Like, It's okay,
you're supposed to take a break." They also believed that we could leverage
existing technology to mental- to manage mental wellbeing,
something that one of the participants has already started to do
by using Google Docs as a self-reflection and journaling tool.
Through this study, we identify
three key insights to addressing mental wellbeing at work.
First, there is a need to identify specific factors beyond the job
demands, control and support factors as outlined in the JDCS model
that lead to poor mental wellbeing and when it becomes
problematic in each specific high strain job profile.
In the case of software engineers, the stress that comes with the job
sometimes is seen as a challenge
and does not necessarily lead to poor mental well-being.
Second, mental wellbeing is a multi-level challenge
that spans individuals, teams and organizations.
Interactions across factors from these levels further
complicate the manifestation and management of mental wellbeing.
Therefore, solutions and interventions should consider a multi-level approach.
Third, there is a need to formally integrate mental wellbeing into work technologies.
For example, software engineers frequently
utilize technologies to facilitate parts of their agile process,
including their daily stand ups and retrospectives.
These technologies can include mental wellbeing features
that not only facilitate mental wellbeing education,
but also team based experience.
It is important to acknowledge some ethical considerations
around implementing mental wellbeing technologies at work.
Our participants brought up skepticism as a reason for not leveraging existing solutions.
Other studies on work productivity tools
also suggest that it is important to consider data privacy
regarding employees mental wellbeing data and users autonomy in
determining the amount of information to provide and the use of their data.
Finally, there are several limitations to the study,
the two primary ones being the small sample size that we had.
And the participant experiences are limited to the United States
of America work culture. I thank my
co-authors and others have helped provide feedback on this work and paper.
Thank you so much for listening.