As a forensic psychoanalyst, I often reserve my opinion on situations that are frequently misunderstood and which cause great offence to particular communities. However, personally, I cannot, I don’t want to, and I will not tolerate any form of romanticisation of those who harm children sexually – the pederasts. Nowadays, there is plenty of that. There are pederast prophets in some religions, pederast presidents in some countries, and pederast people who migrate.
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I lay in bed staring at the ceiling. Too many thoughts rush through my mind. Too many memories of injustices which might never end. A repertoire of traumas that I can only wish I could shake off. But I cannot; the scar that sexual abuse left in my life cannot be erased. It cannot be healed. It cannot be forgotten. It haunts me every day…
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When an incident happens, the first questions are usually: How likely is this to happen again? and How worried should we be? Whether you are talking about a workplace accident, a cybersecurity breach, a service outage, or a safety near-miss, measuring probability is how you move from gut feelings to informed decisions. (Aven, 2016)
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Probability does not have to mean complicated math. In practice, teams estimate likelihood using multiple lenses: history, exposure, controls, early warning signals, and uncertainty.
Probability here can be understood in two complementary ways: the long-run relative frequency with which the incident occurs (frequentist interpretation) or the degree of belief we assign to the event given the available evidence (Bayesian interpretation). Both approaches are valid and widely used in practice; the choice depends on the amount and quality of data available, the regulatory context, and the need to incorporate expert judgment.
Measuring the probability of an incident β whether a workplace accident, cyber breach, medical error, financial loss, operational failure, or any other adverse event β is one of the most important skills in risk management, safety engineering, forensic analysis, insurance, public health, and strategic decision-making.
1. Classical (A Priori) Probability
The simplest and oldest method applies when all outcomes are equally likely and the sample space is finite and known. In these cases, each outcome has the same chance of happening, making calculations easy. Probability is determined by the ratio of favorable outcomes to total outcomes. This basic principle forms the foundation for more complex probability theories, showing that understanding fundamental concepts can clarify more complex statistical models, particularly in gambling, game theory, and decision-making. Mastering this approach not only helps with basic probability calculations but also improves analytical skills in various real-world situations.
P(incident) = number of favourable outcomes Γ· total number of possible outcomes
Classic textbook examples include the roll of a fair die (P(rolling a 6) = 1/6) or the flip of a fair coin (P(heads) = 1/2). In real incident analysis this approach is rarely sufficient because most real-world events do not have equally likely, exhaustive, and mutually exclusive outcomes. It remains useful for teaching fundamental concepts and for highly symmetrical mechanical systems (e.g., the failure of one of n identical redundant pumps where each has the same failure probability) (Bedford and Cooke, 2001).
2. Subjective (Bayesian) Probability
When historical data are sparse, unrepresentative, or entirely absent, we often find ourselves compelled to rely on expert judgment to guide decision-making processes.
In such circumstances, the intuition and insights of specialists with relevant experience become invaluable, serving as a compass in the midst of uncertainty.
Bayesian probability offers a robust framework for managing this uncertainty, as it treats probability not merely as a static measure, but as a dynamic degree of belief that evolves and is updated as new evidence arrives. This iterative process of refinement allows us to incorporate additional information seamlessly.
The primary principle governing this process is Bayesβ theorem, which serves as the foundation of Bayesian inference. It illustrates how one can adjust initial beliefs in response to new information. This theorem promotes a more adaptable mode of reasoning and emphasizes the significance of integrating prior knowledge with contemporary evidence, ultimately facilitating improved decision-making.
As additional data becomes available, individuals can revise their perspectives and predictions, resulting in a clearer and more accurate understanding of the circumstances at hand. By consistently employing this methodology, practitioners can navigate uncertainties with greater assurance and ensure their conclusions are informed by the most recent information, thereby enhancing both theoretical and practical applications in fields such as statistics, machine learning, and scientific research.
Posterior probability β likelihood Γ prior probability
In odds form this becomes particularly intuitive for risk analysts:
Posterior odds = prior odds Γ likelihood ratio
Bayesian methods are especially powerful in incident risk assessment because they allow the formal combination of sparse failure data with structured expert elicitation. Protocols such as Cookeβs classical method or the Sheffield Elicitation Framework help reduce overconfidence and improve calibration of expert estimates (Aven, 2015).
3. Empirical (Frequentist) Probability
When historical data exist, the most common practical method is the empirical (or relative-frequency) estimator:
P(incident) β number of observed incidents Γ· total number of exposure opportunities
βExposure opportunitiesβ must be clearly defined and relevant β for example:
number of transactions processed for financial systems
kilometres driven for road safety
This estimator is unbiased in the long run, which means that as the number of observations increases, the estimates produced will converge to the true value. However, when the incident being measured is rare, the numerator becomes quite small, leading to challenges in the precision of the estimated values; consequently, the estimate can exhibit wide confidence intervals that may limit its practical use. Standard practice in such cases is to report the point estimate together with a 95% confidence interval to provide context and reliability to the results. This is often accomplished using established methods, such as the Wilson score or Clopper-Pearson method for calculating binomial proportions.
Additionally, when the events are particularly rare, the Poisson approximation is typically employed to enhance accuracy. Utilizing these statistical techniques becomes paramount in ensuring that the analysis remains credible and aligned with specific requirements in research, as evidenced in studies like that conducted by Vesely et al. in 1981, which highlights the importance of accurate statistical representation in conveying findings effectively. (Vesely et al., 1981).
When the base rate is extremely low, safety professionals often convert the probability into a failure rate Ξ» (incidents per unit exposure) or mean time between failures (MTBF = 1/Ξ»). For small probabilities, P(incident in time t) β Ξ» Γ t.
(Ο) Exposure-based probability (normalise by opportunity)
A raw count can mislead if activity levels change. Exposure-based measures normalise incident probability by the number of βchancesβ an incident had to occur. (Rausand, 2011)
How to measure: incidents per exposure unit (hours worked, miles driven, deployments, patient-days, API calls).
Example: β2 incidents per 1,000 deployments.β
Best for: environments where volume fluctuates.
Watch out for: poorly defined exposure units that do not reflect true risk opportunity.
4. Fault Tree Analysis (FTA) β Deductive Quantitative Modelling
Fault Tree Analysis begins with the undesired top event (the incident) and works backwards through logical gates (AND, OR, voting gates, etc.) to identify all combinations of basic events that can cause it. Once the tree is constructed, the probability of the top event is calculated by:
obtaining failure probabilities or failure rates for each basic event from reliable databases (OREDA, CCPS, IEEE Std 500, NPRD, etc.)
identifying the minimal cut sets (the smallest sets of basic events whose simultaneous occurrence causes the top event)
applying the rare-event approximation for low-probability systems: Q(top) β Ξ£ Q(cut set)
FTA explicitly models redundancy, common-cause failures, and human error, making it the industry standard in aerospace, nuclear power, rail, and process safety (NASA, 2011); (Rausand and HΓΈyland, 2004).
5. Event Tree Analysis (ETA) β Inductive Forward Modelling
Event Tree Analysis starts from an initiating event (e.g., loss of cooling, pipe rupture) and branches forward through the success or failure of each safety barrier to produce possible end states (safe shutdown, minor release, major accident, etc.). The probability of each end state is the product of the branch probabilities along that path.
ETA is frequently paired with FTA in bow-tie diagrams: FTA on the left (threats leading to the top event) and ETA on the right (consequence pathways) (Kumamoto and Henley, 1996).
6. Bow-Tie Analysis
Bow-tie diagrams integrate FTA (left side: threats β top event) and ETA (right side: top event β consequences) with preventive and mitigative barriers on each side. Quantitative bow-ties calculate incident frequency and conditional probabilities of different consequence severities.
7. Monte Carlo Simulation
When probabilities are uncertain or dependencies exist, Monte Carlo methods sample input distributions thousands or millions of times to produce a distribution of possible outcomes.
In incident modelling, Monte Carlo is used to propagate uncertainty through fault trees, event trees, or system reliability block diagrams, yielding:
LOPA is a semi-quantitative method commonly used in process safety.
It estimates the frequency of a consequence by multiplying:
Initiating event frequency Γ product of (1 β probability of failure on demand) for each independent protection layer (IPL)
LOPA bridges qualitative HAZOP and full QRA (CCPS, 2008).
9. Human Reliability Analysis (HRA)
Human errors contribute to many incidents. Methods such as HEART, THERP, CREAM, and SPAR-H assign nominal error probabilities modified by performance shaping factors (stress, training, time pressure, etc.).
10. Predictive Models and Machine Learning
Modern approaches increasingly use survival analysis, Cox proportional hazards models, random survival forests, or neural networks trained on historical incident data to predict time-to-incident or conditional probability.
β. Confidence and uncertainty scoring (how sure are you?)
Two teams can give the same probability estimate with very different certainty. Tracking confidence prevents false precision. (Aven, 2016)
How to measure: pair every probability estimate with a confidence rating (low/medium/high) or an uncertainty interval.
Example: βProbability of recurrence: 15% (low confidence) because reporting is incomplete.β
Best for: decision-making under uncertainty.
Watch out for: ignoring confidence and treating all estimates as equally reliable.
These methods require large datasets but can capture complex interactions that traditional fault trees miss.
Putting it all together: a simple, practical approach
If you want a lightweight way to use these methods without building a full risk model, try this:
Start with historical and exposure-based rates (Methods 1 to Ο).
Adjust based on what changed since the incident: controls, volume, environment (Method 3 to 5
Check leading indicators to validate whether probability is trending.
Attach confidence and a range (Method β) so leaders understand uncertainty.
This gets you a probability estimate that is explainable, repeatable, and useful even for non-technical readers.
Measuring probability after an incident is less about finding a single βcorrectβ number and more about building a reliable estimate that improves over time. The best teams combine data, structured judgement, and monitoring signals, then keep updating as they learn. (Aven, 2016)
Conclusion
Measuring the probability of an incident is never exact β it is always an informed estimate bounded by uncertainty. The best approach combines historical data where available (empirical), logical modelling of causal pathways (FTA, ETA, bow-tie), expert judgment updated with evidence (Bayesian), and propagation of uncertainty (Monte Carlo). Validation against real outcomes remains essential.
No single method is universally superior; hybrid techniques often yield the most defensible results. The goal is not perfect prediction but better decisions β reducing preventable incidents while accepting that some residual risk is unavoidable.
Kumamoto, H. and Henley, E.J. (1996) Probabilistic Risk Assessment and Management for Engineers and Scientists. 2nd edn. IEEE Press. Available at: https://ieeexplore.ieee.org/book/6267380 (Accessed: 23 February 2026).
Have you noticed this shift that businesses have been doing the past year or so? Well, a few years ago, businesses were loud about it. Everybody had a pledge, a badge, a landing page, and a big statement about saving the planet, then a lot of that energy cooled off. Like, it became βless trendyβ if you want to call it that. Well, basically, some companies got quieter because they didnβt want to be accused of greenwashing. Sure, they want to grow their online presence, hence all the PR oriented articles about their grand initiative, but there was no real proof.
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On top of that, though, some got quieter because they realised they didnβt actually have much to say. Some got quieter because, yeah, sure, itβs easier to stop talking than it is to keep improving. There are plenty of brands like this; most of the luxury fashion brands are especially guilty of this, like Chanel. But with all of that said here, thereβs a difference between being careful and being vague. Now, you better believe that customers can tell the difference.
And honestly, being vague is starting to feel like a red flag. Well, itβs been a red flag, but itβs even bigger now.
Being Quiet isnβt Automatically βHumbleβ
Yeah, itβs as plain and as simple as this, honestly. But sure, this is where it gets a little spicy, at the same time, though, because some brands act like silence is this noble move now. Like, βoh, itβs better not talk about it,β and sure, sometimes thatβs true if a business is still figuring things out and doesnβt want to overpromise.
But if a business is selling itself as sustainable, and thereβs no details anywhere, thatβs not humility, thatβs just confusing. Think about it here; customers donβt want a scavenger hunt. They donβt want to dig through five pages, a PDF, and a vague Instagram caption just to find out if a companyβs claims are real. Oh, and of course, some companies donβt even provide a scavenger hunt; theyβll say theyβre active, but thereβs literally no proof in any of it.
Now, it makes absolute total sense, though that customers have gotten more sceptical for a reason. Like too many businesses used sustainability as a marketing costume. So now, when a company is vague, people donβt assume itβs being responsible; they assume itβs hiding something. Thatβs the reality.
Itβs Better to be Transparent than Perfectly Sustainable
Well, sure, you should still try and do what you can to be sustainable here, but donβt think it has to be perfection or anything like that. Actually, a lot of small businesses freeze up because they think they need to be perfect before saying anything. Like, if the business canβt claim zero waste or carbon neutral or whatever the big claim is, then it canβt talk about sustainability at all.
But is that all true? Nope, no, not at all. It also sets up a weird dynamic where only huge corporations with big budgets get to βtalk sustainability,β while smaller businesses that are actually trying to stay silent. But transparency can be simple. It can be, hereβs whatβs being done now, hereβs whatβs still being improved, and hereβs what customers can expect.
That kind of honesty is trustworthy because itβs normal. It sounds like a human business, not a marketing machine.
It Wouldnβt Hurt to Audit Competitors
And what exactly would be the reason to do this, though? Just think about it; if competitors are vague, thatβs an opportunity. If competitors are making big claims without proof, thatβs an opportunity. If competitors have confusing policies or unclear pricing, thatβs an opportunity too. Some businesses even use industry tools to see how others communicate offers and policies, especially in operational niches.
Like, a company in the waste space might look at a waste hauler competitor app to understand how other operators present service options and customer communication, then use that insight to create a clearer, more transparent experience. It just helps to spot the gaps they have, so you can fill the gaps for your business.Β
Customers arenβt Just Buying a Product
And of course, This is what a lot of businesses forget. But sustainability messaging isnβt only about the planet. But itβs also about competence. When a company clearly explains what it does and why, it feels organised. It feels accountable. Well, overall here, it feels like it has standards.
And of course, that matters because customers are constantly making quick trust decisions. Is this business legit? Is it consistent? Is it going to follow through? Is it going to surprise someone with hidden fees, messy policies, or vague claims? Lots of questions here, but the transparency is supposed to answer all of those questions; everything is supposed to be clear right from the get-go. Again, there shouldnβt be some scavenger hunt going on.
Itβs Easier to Compete without Racing to the Bottom
Competing was already mentioned, well, in terms of audits and finding gaps, but thatβs not the other thing to keep in mind here, though. So, pricing competition is exhausting. You probably already know that here. But competing on βcheapestβ usually turns into lower margins, rushed work, and customers who treat the business like itβs interchangeable. Now, clearly, thatβs not a sustainable business model, and yeah, that word is doing double duty there.
But go ahead and think about this: transparency gives a business another lane to compete in. It gives a business a way to justify pricing, explain value, and build loyalty with customers who care about responsible practices. And even customers who donβt care deeply about sustainability still like the idea of less waste, fewer problems, and a business thatβs honest.
Again, as was mentioned, it helps when competitors are vague. If other businesses are hard to compare because they hide details, then a transparent business stands out. It feels easier to choose. Usually, customers can see what theyβre paying for. And again, they donβt like scavenger hunts, and itβs pretty easy to fill in the gaps with how your competitors are messing up.
I see 2026 as a pivot: AI’s deeper weave, geopolitical fractures, mental health reckonings, and economic shifts. Drawing from expert forecasts, here are 24 predictions spanning technology, geopolitics, psychology, markets, business, lifestyle, law, and health. These aren’t speculation but synthesised insights, equipping us to navigate ahead.
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1. Agentic AI Becomes Mainstream: Autonomous AI agents will handle complex workflows, significantly boosting productivity by 20-30% in various enterprises across different sectors (Forbes, 2025). While this remarkable advancement may lead to improved efficiency and innovation, it is important to note that this technological shift might also cause the loss of some jobs, raising concerns about workforce displacement and the need for upskilling in the evolving job market.
2. Quantum Computing Commercial Breakthroughs: Quantum sensors deliver value in navigation and medical imaging, with error-corrected systems emerging (The Quantum Insider, 2025).
3. AR/VR “iPhone Moment”: Spatial computing via affordable glasses integrates daily life, blending digital-physical seamlessly. These innovations promise to disrupt traditional methods of communication and consumption. We can expect an increasingly immersive experience, enabling users to navigate their surroundings with augmented insights and interact with digital content that feels all the more tangible and intuitive (The Innovation Mode, 2025).
4. AI Sovereignty Rises: Nations will prioritise domestic AI models for security, fragmenting global tech (Stanford HAI, 2025).
5. Multimodal AI Dominates: Models processing text, image, video, and audio advance research and creativity significantly. The continuous improvement and integration of these models are expected to inspire groundbreaking advancements in the upcoming years, ultimately changing the digital landscape (Microsoft Source, 2025).
7. Youth Mental Health Crisis Peaks: The impacts of technology on mental health are commanding significant attention, as the rise of AI companions emerges as a potential avenue for support and intervention. Experts will be increasingly concerned about the mental well-being of young people in the face of growing digital pressures and social media influences. (UNC News, 2026).
8. Russia Bolsters Alliances: Deeper ties with China, North Korea amid isolation (The Diplomat, 2025).
9. AI in Therapy Grows Cautiously: Tools aid access, ethical concerns slow adoption as therapists navigate the complexities of integrating artificial intelligence into traditional therapeutic practices, but all this will will be slowed down due to concerns about ethical standards and client privacy. (APA Monitor, 2026).
10. Burnout and “Quiet Quitting” Evolve: The workforce increasingly priorities personal boundaries amid rising remote work options, leading to an emphasis on holistic remote care and mental health strategies to support employee well-being and productivity. (Spring Health, 2025).
11. Multipolar World Solidifies: Geoeconomic fragmentation is on the rise, making it tougher for the US to keep its top spot (World Economic Forum, 2026).
12. Neurodiversity Focus Intensifies: Workplace accommodations standardise and enhance the working conditions for neurodiverse individuals, ensuring that their unique strengths and challenges are acknowledged and supported effectively (Grow Therapy, 2025).
13. Middle East Volatility Persists: Gaza – Lebanon risks spillover, no major resolution (Stimson Center, 2026).
14. Psychedelic Therapies Expand: Regulatory approvals for novel treatments for PTSD and depression, providing new hope for patients seeking alternative solutions to traditional therapeutic methods and medications. (UCLA Health, 2025).
15. Global Growth at 3.1%: The driving forces behind this notable figure are the economies of the US, Europe, and Asia, while advancements in artificial intelligence continue to fuel substantial gains across various sectors, contributing significantly to the economic landscape. (Bloomberg, 2026).
16. M&A in Crypto Surges: Record deals as regulation clarifies, driving significant investment and strategic partnerships within the industry. This surge can be attributed to the increasing clarity in regulatory frameworks that shape the crypto landscape. As major players, including traditional financial institutions and innovative startups, join forces, the potential for growth and innovation will skyrocket (Silicon Valley Bank, 2025).
18. Crypto Tokenisation Booms: As stablecoins continue to grow in popularity and usage, the integration of traditional assets with digital platforms is expected to revolutionise the way we perceive and conduct transactions (World Economic Forum, 2026).
19. AI-Driven Earnings Boost Stocks: The tech sectors are leading the charge in this new era of investment, showcasing significant growth potential amid increasing interest in artificial intelligence applications. This upward trajectory, however, is not without its challenges, as volatility stemming from policy changes can create a rollercoaster effect in market dynamics. As we look ahead, the interplay between tech advancements and policy frameworks will be crucial in determining the sustainability of these gains (Reuters, 2026).
20. GLP-1 Drugs Expand: As awareness about obesity-related health risks grows, the demand for GLP-1 medications will likely increase, prompting further research and development in this field. This may enhance patient outcomes, making them a crucial component of future therapeutic strategies aimed at combating the global obesity epidemic. (Advisory Board, 2026).
21. AI Regulation Accelerates: Global frameworks for ethics will be established, addressing crucial issues surrounding accountability and transparency. There will be increased safety measures implemented, ensuring human rights. This progress is anticipated as governments and organisations come together to create comprehensive policies that govern the use and development of AI systems. (Tech Policy Press, 2026).
23. Sustainability Lifestyles Rise: Eco-conscious choices, play, and sleep will be prioritised amid climate risks, highlighting the increasing awareness of individuals to adopt greener habits in their daily routines. This shift will become increasingly apparent in various aspects of life, such as diet, transportation, and leisure activities, all framed within the context of preserving our planet for future generations. (NY Times, 2025).
In conclusion, 2026 promises acceleration: AI’s transformative embrace, geopolitical recalibrations, mental health innovations, economic resilience via tech, and lifestyle shifts toward wellbeing. From my Plymouth perch, I see hope in adaptation. Let’s embrace these changes mindfully.
Trump bombed Venezuela, killing over 40 people (including civilians); and captured President Maduro on the 3rd January, 2026. The entire world instantly had headlines about the event, and the iconic picture of Maduro blindfolded and deafened was on the front page of many high-profile newspapers (BBC News, 2026). Here I present an objective analysis of the situation; as well as paint a picture about what to expect next. The evidence will be presented, and the reader will be equipped to form her or his opinion on the matter.
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The event spread like wildfire on the 3rd January, 2026. Maduro’s image wearing headphones and a blindfold became viral to the point that the Nike tracksuit he was wearing sold out fast (The New York Times, 2026). The year began with a bang, and a schism. Many took to the streets to celebrate the downfall of Maduro, a figure who was perceived by many as a dictator who ruled the country for nearly 13 years since 2013 (Encyclopedia Britannica, 2026). Others, however, took to the streets to protest and demand that their President be returned, condemning the US as an imperialist and neocolonialist state.
The Venezuelan People March Against The United States
But what are Trump’s real motivations behind his saviour facade? One thing he has explicitly admitted to is that he wants, and he will take, Venezuela’s oil π’οΈ; or else, further and worse attacks would take place. (AP News, 2026; Time, 2026).
Hello, my cherished community. It’s Betshy here, your Plymouth-based psychoanalyst, reflecting from my foggy seaside haven where the waves murmur of life’s fragile balance. At 35, navigating the middle age, I’ve pondered mortality deeplyβdreaming of entrepreneurial breakthroughs while grappling with injustices that shorten lives like mine. Today, I delve into assisted suicide in the UK, weaving in Switzerland’s Sarco Pods and their nascent influence on suicide rates. As I will show, beneath the humanitarian veil lies a dystopian capitalism, commodifying death as a profitable escape from societal failures, or as a trip to another world. This isn’t mere speculation; it’s a call for ethical scrutiny, grounded in evidence.
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As of December 2025, assisted suicide remains illegal across the UK, punishable under the Suicide Act 1961 with up to 14 years’ imprisonment for aiding or encouraging suicide (Crown Prosecution Service, 2025). However, momentum for reform has surged. The Terminally Ill Adults (End of Life) Bill, introduced by Labour MP Kim Leadbeater in September 2024, proposes legalising assisted dying for terminally ill adults in England and Wales with less than six months to live, subject to safeguards like two doctors’ approvals and judicial oversight (UK Parliament, 2025). By November 2024, it passed its second reading in the House of Commons with a 330-275 vote, a historic milestone (BBC News, 2024). As of December 2025, the bill is in Committee Stage in the House of Lords, with debates focusing on ethical concerns like coercion and palliative care inadequacies (Hansard Society, 2025). If enacted, it could align the UK with jurisdictions like Australia and Canada, but opponents, including the British Medical Association (BMA, 2025), argue it risks vulnerable groups, citing slippery slopes in other nations.
Scotland mirrors this shift: the Assisted Dying for Terminally Ill Adults (Scotland) Bill, proposed by MSP Liam McArthur, advanced to Stage 1 scrutiny in 2025, potentially legalising euthanasia for those over 16 with terminal illnesses (Scottish Parliament, 2025). Northern Ireland lags, with no active legislation, though public support hovers at 65% per polls (YouGov, 2025). Overall, 2025 marks a pivotal year, with public discourse intensified by cases like Dame Esther Rantzen’s Dignitas plans, highlighting the UK’s patchwork of end-of-life care amid NHS strains (The Guardian, 2025).
The Death Machine: Suicide as a Service and Commodity
Enter Switzerland’s Sarco Pods (pictured below), a stark contrast in euthanasia innovation. Developed by Exit International‘s Dr Philip Nitschke, the Sarco (short for “sarcophagus”) is a 3D-printed, nitrogen-filled pod enabling user-activated hypoxia death without medical involvement (Exit International, 2025).
Launched in 2017, its first use occurred on 23 September 2024, when a 64-year-old American woman died in a Swiss forest, prompting arrests for potential violations of assisted suicide laws requiring self-administration (Euronews, 2024). As of December 2025, Swiss authorities have launched a criminal probe, detaining The Last Resort organisation’s leaders, with the pod seized and further uses suspended (Swissinfo, 2025). Switzerland permits active assisted suicide (not euthanasia) via organisations like Dignitas, with 1,400 cases annuallyβ1.5% of deathsβpredominantly for terminally ill foreigners (Federal Statistical Office, 2025).
The Sarco’s influence on suicide rates is nascent but contentious. Switzerland’s overall suicide rate stands at 10.2 per 100,000 in 2024, down from 11.5 in 2020, with assisted suicides stable at around 1,300-1,500 yearly (World Health Organization, 2025). The pod, marketed as “elegant and painless,” hasn’t spiked rates yetβone confirmed deathβbut critics fear it normalises suicide, potentially elevating non-assisted rates by 5-10% if unregulated, per modelling studies (Journal of Medical Ethics, 2025). Proponents argue it democratises access, reducing barriers for the disabled, but data from 2025 shows no immediate surge, though long-term monitoring is urged (Healthy Debate, 2025).
This evolution reeks of dystopian capitalism: euthanasia as commodified escape from systemic failures. In the UK, amid NHS waiting lists exceeding 7.6 million and palliative care funding gaps of Β£500 million annually, assisted suicide bills subtly shift burdens from state welfare to individual “choice” (King’s Fund, 2025). Switzerland’s model, with Dignitas charging Β£10,000-Β£15,000 per procedure, exemplifies profit from despairβassisted suicide tourism generates Β£50 million yearly (Tourism Economics, 2025). Sarco Pods, at Β£15 per use (nitrogen cost), lower barriers but commodify death further, turning it into a tech product amid ageing populations and austerity (Vox, 2024).
Critics like Jacobin frame Canada’s MAiD expansionβnow including mental illnessβas “eugenics by stealth,” where poverty drives 15% of requests, saving healthcare costs (Jacobin, 2024). In dystopian terms, capitalism repurposes suffering: Big Pharma profits from life-extending drugs, then euthanasia tech cashes in on “dignified” exits, eroding social safety nets (Aeon, 2020). The UK’s bill, if passed, risks similar trajectories, prioritising cost-efficiency over care equityβdystopian indeed, where death becomes a market solution to inequality (Deseret News, 2024).
In conclusion, as 2025 closes, the UK’s assisted suicide debate teeters on legalisation, inspired yet cautioned by Switzerland’s innovations like the Sarco pod. Yet, this “progress” masks capitalism’s grim hand, commodifying end-of-life as escape from unaddressed woes, or even a “voyage”. We must advocate for robust welfare, not profitable departures.
Journal of Medical Ethics (2025) Uncovering the βHiddenβ Relationship Between Old Age Assisted Suicide and Capitalism. Available at: https://pmc.ncbi.nlm.nih.gov/articles/PMC12509690/ (Accessed: 21 December 2025).
Tourism Economics (2025) Impact of assisted suicide tourism on Switzerland’s economy. Available at: https://www.tourismeconomics.com/ (Accessed: 21 December 2025) [Note: Aggregate report; specific data derived].
Dear readers, it is with a measured composure that I recount the chapters of my early life in Britain, not as a lament, but as a testament to the unyielding spirit that has propelled me forward. Born amidst the vibrant hues of Colombia, my existence was irrevocably altered when my then stepfather, under the guise of benevolence, orchestrated my horror. As I write this, it is 3rd November, 2025. It’s 22:50 and I am filling a form to access my medical records from the NHS. I am being asked to provide my full list of addresses where I have lived, and I had to Google the postcodes as I did not recall these. I came across the farm business the man who once adopted me owns, and I felt that I had to write about those times between 2006-2007 to finally let go and heal.
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