No sick days? How remote work is changing the way we work when ill

By Joe Stafford, University of Manchester

New research calls for a rethink of ‘presenteeism’ as technology makes it possible to work while unwell from almost anywhere.

Image: Pavel Bekker - Unsplash

Working from home and digital technology may be making it easier for employees to work while ill and blurring the boundaries between work and home, according to a new study.

The paper, by Professor Sir Cary Cooper of The University of Manchester, Professor Luo Lu of National Taiwan University and Professor Shu-Fang Kao of Soochow University, argues that traditional definitions of presenteeism no longer reflect the realities of modern working life.

Working from home doesn’t mean switching off

The researchers say the widespread adoption of remote and hybrid working means many employees no longer need to physically go into the workplace to do their jobs while unwell. Instead, they can remain at home, respond to emails or messages, attend virtual meetings or carry out urgent tasks while ill.

The study argues that this creates new challenges for understanding the impact of an increasingly ‘always-on’ workplace culture.

Key findings

• Presenteeism is no longer simply about going into work while sick - people can now work while unwell from home or elsewhere.
• Digital technology can make it harder to switch off, with employees potentially feeling pressure to remain available through email, messaging platforms and other workplace technology even outside normal working hours.
• Mental health needs to be recognised alongside physical illness when considering why people work while unwell, as existing research has focused heavily on physical sickness.
• Working while ill can take different forms - from completing a normal working day remotely to doing only urgent tasks or checking emails intermittently.
• Employers should consider whether workers have flexibility to reduce their hours or workload when ill, rather than treating working while sick as an inherently good or bad behaviour.

Presenteeism is no longer just about physical presence

The researchers propose redefining presenteeism as the “behaviour of attending work in the state of suboptimal health, with varied location choices and degree of engagement”.

This means that presenteeism should be understood not simply in terms of whether someone turns up to work, but also where they work, what health problem they are experiencing and how much effort they put into working.

The authors argue that existing approaches to measuring presenteeism are too narrow because they generally focus on how often people go to work despite being ill, or on the extent to which illness affects their productivity. Instead, they propose a three-dimensional framework based on health, location and engagement.

Eight ways of working while unwell

The first dimension considers whether presenteeism is triggered by a physical or mental health problem. The second considers whether someone works on-site or remotely. The third considers whether they work a full schedule with normal levels of engagement or reduce their hours and effort. Together, these dimensions produce eight different forms of presenteeism.

For example, an employee with a physical illness might go into the workplace and work a full day, while another might stay at home and only deal with urgent requests. Someone experiencing anxiety or depression could similarly work either remotely or on-site, with either full or reduced engagement.

The researchers say this approach could help distinguish between situations where working while ill may be harmful and situations where employees are able to adapt their work in ways that allow them to protect their health while meeting essential responsibilities.

The paper also highlights concerns about inequality in access to flexible working. Remote working is not available to everyone, meaning that some workers may be able to adapt their working arrangements when ill while others have little choice but to attend their workplace.

What the experts say

“The world of work has changed dramatically, but our understanding of what it means to work while sick has not necessarily kept pace with this transformation,” said Professor Cary Cooper, 50th Anniversary Professor of Organisational Psychology and Health at The University of Manchester. “Technology has given people much greater flexibility over where and when they work, but that flexibility can become a trap if employees feel they have to be available all the time.”

“Someone working from their sofa while ill, answering emails and dealing with urgent requests, is still working while sick - even though they have not physically gone into the workplace. The challenge for employers is to make sure that technology and flexible working are used to support people’s health, rather than creating an expectation that they should always be available.” — Professor Sir Cary Cooper.

Making flexibility work for employees

The authors argue that future research should examine whether giving employees greater control over their location, working hours and workload can make presenteeism a more adaptive behaviour. They also call for greater attention to mental health-related presenteeism, which they say has been relatively neglected by existing research.

The researchers stress that working while sick should not automatically be regarded as either positive or negative. Instead, they argue that understanding how someone works while unwell - and the circumstances surrounding that decision - is essential to understanding its effects on health and performance.

The paper concludes that organisations need to strike a balance between maintaining performance and protecting employee wellbeing as digital technology continues to reshape the workplace.

Publication details: The paper was published in journal Health Psychology Research. DOI: https://doi.org/10.36922/HPR026130066.

Reviewed by Irfan Ahmad.

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New Meta safeguards shift burden to parents, UEA expert warns

By University of East Anglia

Image: Mariia Berezovsky - Unsplash.

New Meta measures may deepen inequalities by shifting responsibility onto parents and young people – according to an expert at the University of East Anglia.

The tech giant has agreed to a historic $18 billion settlement with dozens of US states, bringing an end to a high-profile federal trial.

The lawsuit accused Meta of deliberately designing addictive platforms that harmed children's mental health.

The proposed settlement introduces new youth safeguards, but social media expert Dr Harry Dyer says that such measures may deepen digital inequalities.

Dr Harry Dyer, Associate Professor in Education at the School of Education and Lifelong Learning at UEA, said: “Meta has spent a long time insisting that their product is safe for young people, despite the large amount of research that has detailed online harms for children.

“Whilst the settlement allows Meta to deny wrongdoing, it also suggests that there is a clear need to change their platforms and also suggests they were reluctant to continue with the public transparency that the trial would continue to bring.

“The proposed changes to the platform as part of the settlement include default daily time limits of two hours and nighttime blocks for users that are deemed to be under 18 by the platform, which will seemingly only be liftable by parents.

“Whilst this is promising, our recent work has revealed that engaging with these platforms as a parent require a high level of digital literacy and citizenship from parents and children.

“Our recent work has found that the burden of understanding these platforms mapped onto existing class and educational inequalities, and exacerbated harms on the most vulnerable young people.

“The proposed protections by Meta will likely not be evenly distributed and will also likely be linked to different level of cultural and technological capital.

“As usual, users are being made responsible to navigate these changes, and to make informed choices about which design features are better, whilst the platform evades the accountability that an ongoing trail would likely bring. Structural risk here gets repackaged as individual responsibility for parents and children to navigate.”

Reviewed by Irfan Ahmad.

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Consumers Are More Likely to Use Free AI Chatbots Than Paid Versions

By Felix Richter, Statista

Nvidia’s latest results once again underlined that the AI boom shows no signs of slowing down. Last quarter, the company returned to triple-digit growth, doubling revenue and net income, the latter already exceeding $100 billion halfway through the current fiscal year. And yet, behind the extraordinary spending on chips and data centers looms a harder question: when and how will all that investment pay off?

One reason for the skepticism is that much of the AI economy still looks suspiciously circular. Hyperscalers are investing hundreds of billions of dollars to build AI data centers, fueling Nvidia’s extraordinary growth. Nvidia partially invests those billions in AI companies who in turn use that money to rent computing power from the aforementioned hyperscalers. What’s missing in this equation is an influx of money from outside of this loop or at least a clear idea where this money will eventually come from. As of today, many of the most popular AI tools – at least the consumer-facing ones – remain free to use to a certain extent, raising the question of who is footing the bill.

Survey data from Statista Consumer Insights confirms this observation. In all five markets surveyed, respondents were significantly more likely to say they usually use free AI chatbots than to have access to a paid version. The difference is particularly pronounced in Brazil, Germany and the United States – suggesting that widespread experimentation with AI has not yet translated to willingness to pay for it.

Fact-checked by Irfan Ahmad.

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China’s AI and tech breakthroughs are built on decades of work. Here’s what other nations can learn

By Frederik von Briel, The University of Queensland

Image: Zhe ZHANG - Unsplash

In the late 1940s and early 1950s, the United States and the Soviet Union were locked in a race for technological and ideological supremacy.

The Cold War was still in its early years and each side was intently focused on the other’s scientific and military progress. The US believed it was firmly in front.

So the Americans were caught almost completely off guard in October 1957, when the Soviet Union successfully launched Sputnik-1, the world’s first ever artificial satellite, into orbit.

Today, the US is locked in competition with a different superpower: China. In recent years, China has given the world some surprise breakthroughs of its own – from advanced hypersonic missiles to cutting-edge computer chips and powerful artificial intelligence (AI) models.

The question is, how did China get there – and what can other countries like Australia learn from it?

Decades in the making

A recent study by the National Bureau of Economic Research (NBER) in the US sought to better understand Chinese innovation by examining about 14 million Chinese patent publications from between 1985 and 2023.

Its findings suggest these modern “Sputnik moments” were not isolated strokes of luck, but were instead decades in the making.

The study noted that Chinese patent activity in critical technology areas (as defined by the US Department of Defense) has been growing.

The number of new patents has steadily increased from fewer than 10,000 annually in the early 2000s to more than 400,000 in 2022.

A related report by the Australian Strategic Policy Institute suggests China now leads globally in 69 of 74 critical and emerging technology domains.

Quality and quantity

When looking more closely at the recent NBER study, a few interesting insights emerge.

First, it reveals China hasn’t just been filing an increasing number of low-quality patent applications. On a measure that tracks whether novel ideas influence later inventions, Chinese patents steadily gained ground relative to US patents.

Second, it hasn’t just been a few “national champion” organisations driving China’s patenting activity. Quite the opposite.

The ten largest owners of critical-technology patents only held 4% of all Chinese patents in 2022. That’s markedly different to the US, where the ten largest owners held 20% of all patents.

And there are big differences on a third point, too – the role of universities. While the private sector accounts for 58% of China’s critical-technology patents, 27% of patents originate from Chinese universities.

That’s in stark contrast to the US, where universities account for only 3% of critical-technology patents.

The role of public research

History suggests that this pattern may not be unique to China. In her 2013 book, The Entrepreneurial State, US-Italian economist Mariana Mazzucato challenges the myth that individual entrepreneurs and firms develop major breakthroughs on their own.

She argues many technological breakthroughs in the US depended on the long-term accumulation of inventions and capabilities across various actors. And importantly, that publicly funded research often also played a major role.

For example, drawing on the iPhone as a breakthrough product, Mazzucato shows that many of the technologies that made the iPhone possible – from touchscreens to GPS and even its voice assistant Siri – had been developed across different, often publicly funded, research institutions before the iPhone was even conceived.

How does Australia compare?

Australia has had a strong track record of invention and scientific discovery in the past. But we’re increasingly struggling to keep pace with the world’s technological leaders.

Australia does still punch above its economic weight in some specific critical technology areas.

For instance, since 2000, Australia accounts for almost 9% of patents in critical-metals refinement for batteries. Australia also ranks 12th globally in the number of quantum-computing patents.

And Australia has had some big innovation success stories in the past. A form of solar cell developed at UNSW in the 1980s now powers more than 90% of new solar panels around the world.

The wireless local area network (WLAN) technology that made modern wifi fast and reliable was also developed here, by the publicly funded CSIRO.

A troubling trajectory

However, Australia’s current trajectory in technology development is somewhat concerning.

Relative to the size of its economy, Australia’s spending on research and development has fallen from 2.24% of gross domestic product in 2008–09 to 1.69% in 2023–24.

Simply increasing spending on research and development may not be enough. New inventions and capabilities also need businesses with the people, expertise and resources to absorb them and develop them further.

Here, Australia appears comparatively weak. Australian businesses employ only around 3 researchers per 1,000 employees, compared with an OECD average of 6.5.

This suggests Australia might have less capacity to turn technological breakthroughs into commercially viable products and services compared with the rest of the world.

Lessons for other countries

This doesn’t mean Australia or other middle powers need to invent and build every critical technology domestically to benefit.

Wireless LAN technology generated around A$430 million in licensing revenue. It is also embedded in more than 15 billion devices globally. This is despite Australia never becoming a dominant producer of wifi hardware.

One key takeaway from China’s innovation success is that by the time a technological breakthrough becomes visible globally, the domestic capabilities behind it may have been accumulating for decades.

Trying to develop them only when they suddenly become critical may be too late.

Frederik von Briel, Associate Professor in Strategy and Entrepreneurship, The University of Queensland

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Reviewed by Irfan Ahmad.

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Study Examines How Personality Traits Relate to Phubbing Behavior

By Inderscience

Image: Julie Ricard - Unsplash

Personality traits may help predict whether a person will ignore others while using their smartphones, according to a study of healthcare workers in the International Journal of Business Innovation and Research.

The study involved 177 co-workers and looked at how prevalent 'phubbing' is. Phubbing is a portmanteau of the words 'phone' and 'snub' and is the practice of ignoring someone in preference to using one's phone despite being in a situation in which face-to-face conversation and interaction would be the norm. The study might help social scientists and others understand better the notion of smartphone addiction.

The research assessed participants using the so-called Big Five personality traits - openness to experience, conscientiousness, extraversion, agreeableness, and neuroticism. Personality and phone addiction were self-reported, while co-workers provided evidence of phubbing by others. The team found that people scoring higher in openness, extraversion and neuroticism were more likely to display phubbing behaviour. Conscientiousness, a tendency towards responsibility, self-discipline, and consideration of one's obligations, was associated with less phubbing. There was no significant relationship between phubbing and agreeableness, oddly enough.

If a condition we might call mobile phone addiction exists, then it might be described as an inability to refrain from phone use despite the potential for offline human interaction. To be a true addiction, there has to be associated harm, and the researchers suggest that psychological harm may well occur, either to the phubber or the phubbed. This phenomenon partly explains the relationships between personality and phubbing, the team reports. That said, the researchers emphasise that understanding individual differences behind problematic smartphone use could help organisations address antisocial phone behaviour without treating smartphone use itself as inherently harmful.

Khan, M.N., Shahzad, K. and Shafi, M.Q. (2026) 'This or that, which coworker phubb more; association between personality traits and phubbing behaviour through mobile phone addiction', Int. J. Business Innovation and Research, Vol. 40, No. 4, pp.466–487.
DOI: 10.1504/IJBIR.2026.155659.

Reviewed by Irfan Ahmad.

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Can baby monitors protect children without data security risks?

Erika Sanchez-Velazquez, Anglia Ruskin University

Image: Jakub Żerdzicki - Unsplash

For parents, keeping children safe is not only instinctive but a priority. We babyproof the house, we check the car seat twice, we wake up in the middle of the night at the smallest sound.

As technology has evolved, it also offered a promise to help parents watch over our kids even when we are not in the same room as them.

Throughout the different stages of kids’ lives, parents now rely on different technology to help keep kids safe. This tech now includes trackers, location-sharing apps and parental control apps. But at the earliest stages of our kids’ lives, we rely on baby monitors.

Monitors allow parents to keep an eye on babies without having to be in the same room. The first model was the Zenith Radio Nurse, released on sale in 1938.

More recent products have many advanced features, including contactless breathing and vital signs monitoring, AI-powered sleep analytics, cry and sound analysis, and environmental monitoring. However, these new levels of functionality depend on ever more sensors, more cloud processing, and more data being collected on children.

Parents now have to weigh up the privacy of the data these devices collect, as well as the security risks they bring into the home. In May 2026, a security researcher in France named Sammy Azdoufal decided to probe some of the weaknesses behind budget smart cameras.

Azdoufal discovered that he could pull up other people’s live and recorded footage without needing to guess a password or do anything that really counts as “hacking”. His findings, reported by CyberNews, traced back to a shared system used behind the scenes by more than 300 different camera brands sold on retailers such as Amazon.

He estimated that more than one million devices were affected, many of them baby monitors in bedrooms.

Shared vulnerabilities

What most parents don’t realise is that the brand on the box is rarely the company that built the camera or wrote its software. Many monitors, even from well known sellers, come out of a small number of factories. They get sold under dozens of different names, so one weakness underneath can affect them all.

The cybersecurity operations company Rapid7 published some of the first well known research into these cameras years ago, and a 2026 academic study found the same pattern repeating in newer devices and their apps.

Regulators have started responding. Updated in 2022, new cybersecurity rules under the EU’s Radio Equipment Directive have applied to any internet-connected wireless device sold in the EU. These new rules require manufacturers to protect users’ data, stop devices being hijacked and guard against fraud.

Some manufacturers are taking steps to improve security. For example, the baby monitor manufacturer Owlet announced in late 2025 that its newest model was the first baby monitor awarded the SGS Cybersecurity mark. This mark requires manufacturers to include encryption, a unique passwords and a channel for researchers to report flaws. The certification gives parents something concrete to look for.

Security is only half of it. Even a monitor that’s never hacked can still raise data privacy issues. Many collect more than video. They gather information on feeding times, as well as sleep and growth data, and share it with third parties for analytics or advertising under vague “partner” clauses.

Sleep pattern data from a baby monitor isn’t classed as medical information under GDPR, so companies can use it for analytics or advertising unless parents opt out. This data often isn’t as anonymous as companies claim once combined with other details.

Cry detection and sleep analysis add to this, since both usually mean sending audio and video to the cloud, where it may also help train a company’s algorithms.

Because these features depend on cloud processing rather than staying on the device, the data often leaves the home network, increasing both privacy and security exposure.

Privacy and long-term protection

The UK has rules covering both sides of this. The Product Security and Telecommunications Infrastructure Act, in force since 2024, bans default passwords and forces manufacturers to say how long they’ll support a device.

On the data side, the UK goes further than most countries through the Information Commissioner’s Office Children’s Code, which applies to any connected device likely to be used by children.

The code requires the highest privacy settings by default, and gives parents a genuine right to see, or delete, what’s been collected, backed by fines of up to 4% of global turnover. Most parents just don’t know to ask.

None of this means every camera monitor is dangerous, or that the reassurance they offer isn’t real. But the trade-off keeps showing up in the research. Internet-connected monitors carry more risk than simple closed ones that talk directly between two devices in your home.

Treat the password like a banking one, keep the app updated, and check how long the manufacturer will support it. The bigger issue isn’t something one password can fix. The underlying issues in the industry mean new vulnerabilities will keep appearing, so the safest path is staying informed and choosing devices with transparent security and support policies.

Erika Sanchez-Velazquez, Deputy Head of School, Computing and Information Science, Anglia Ruskin University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Fact-checked by Irfan Ahmad.

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Can you teach yourself to detect AI writing? Maybe

By Celeste Rodriguez Louro, The University of Western Australia

Image: Jess Morgan - Unsplash

AI is quietly reshaping writing. It’s not just that prose is changing — the very structure of written language is genuinely changing too.

It’s not just a new technology.

It’s not a fad.

It’s an undeniable revolution.

Did this article’s introduction seem AI generated? What made you feel that way?

The truth is, it was not AI generated; I wrote it to emulate synthetic prose, the kind you get when a large language model (LLM) organises words into text.

That kind of prose suddenly seems to be everywhere: on low-quality websites built to accumulate advertising clicks, in social media captions chasing engagement, and even book manuscripts. Crime novelist Jerry Falade recently lost a book deal worth more than $2 million after his own literary agents said they could no longer faithfully determine how his manuscript had come together. Hachette pulled the horror novel Shy Girl in March this year under similar suspicion.

Before generative AI, we could generally assume that written text had been composed by a human. This is no longer the case. So how can we spot AI-written text?

There is no single giveaway. Instead, there are patterns: particular words, sentence structure and expressive habits that appear repeatedly in AI-generated prose. LLMs are also updated rapidly and efficiently so what gives away synthetic text today may no longer do a few months from now.

Quiet, quietly and genuinely

The word quietly is a significant giveaway. Search interest in the word has climbed steadily on Google Trends since December 2021, though this measures searches rather than appearances in writing, so it is suggestive rather than proof.

What did change over that period is how many people began writing with the help of LLMs, and these models do seem to reach for quiet and quietly more than the average human writer.

Genuinely is a similar story, and a more anecdotal one. I notice it overused in AI output, Claude included, more than I would expect from a person writing casually. No one has run a rigorous study on that specific word yet.

The documented version of this pattern is vocabulary that has spiked in scientific writing since 2022: delve, meticulous, underscore, boast and intricate all appear far more often in scanned PubMed abstracts than before, in a pattern researchers have tied directly to ChatGPT-style phrasing.

None of these words is proof on its own. But when words like quietly, genuinely, delve and meticulous appear alongside the same polished sentence framing, it starts sounding like a shared house style among LLMs, rather than an individual voice.

A house style of its own

The “not X, but Y” construction is another part of that house style, alongside a fondness for the rule of three.

It also favours taking a modest claim and then immediately escalating it. For example: “This article presents a useful perspective on language. It fundamentally changes how we think about what it means to write”. Human writers have always used it, but generative AI produces the pattern with remarkable consistency.

Then there’s the em dash, the earliest and most mocked AI tell. It never held up particularly well. One writer ran the same prompt through six chatbots and got eight em dashes from ChatGPT in 573 words, but none from Gemini or Meta AI.

This reaffirms the lesson that, while individual clues are unreliable, their accumulation matters.

Lengthy text

Another clue to generative AI use is text length.

A reporter for The Atlantic describes how, after a driver crashed into her in Johannesburg, his frantic and incoherent behaviour at the scene gave way to a lengthy text written in polished prose only half an hour later. When she later contacted a mechanic whose texts had previously been filled with shorthand, his reply came back in the same distinctive AI voice.

Human language is shaped by the pressures of effort and time. Generative AI removes much of that cost. Realistically, a person might text: “Sorry, running late. Traffic is awful. Be there in 20”.

AI can swiftly turn that into a paragraph explaining the unexpected traffic congestion, expressing sincere regret and thanking the recipient for their patience. Nothing in that longer version is necessarily wrong. It’s simply doing far more linguistic work than the situation generally requires when time is of the essence.

The dangers of AI detection

Some people believe AI detection software can identify AI-generated text. However, these systems are far from perfect and can worsen existing biases.

A 2023 Stanford study found that seven popular AI detectors falsely identified an average of 61% of essays written by non-native English speakers as AI generated, with one tool flagging 97% of them.

AI detection has evolved since then, and more recent research presents a more complicated picture: current detectors can perform better in some settings, yet remain vulnerable to evasion and still produce false positives.

At the same time, the question is shifting beyond detection altogether: the European Union is now demanding that all AI-generated content is labelled or watermarked.

Are LLMs changing the way we write?

A new study in Nature Human Behaviour, analysing more than 880,000 Reddit posts, news articles and academic pieces, found that the spread of LLMs was associated with less variation in writing style.

Knowing who wrote something matters because language lands differently depending on who it comes from. Is the person trustworthy? Knowledgeable? Funny? Younger? Are they real?

The other reason is dexterity. Humans are excellent at problem solving and creativity, and those abilities appear in the peculiarities of individual language: an unexpected word, a unique comparison, a sentence structure that may reappear across writers but not en masse, as is the case with synthetic text.

If the prose across our media begins to sound as though it has been cut from the same automatic, uncreative, underwhelming and repetitive mould, we lose individuality, nuance, and our trust in the authenticity of the written text and its writer.

Celeste Rodriguez Louro, Associate Professor and Director of Language Lab, The University of Western Australia

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Fact-checked by Irfan Ahmad.

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