This morning, I sat quietly at the back of a room. Well, “quietly” (in the way a person with ADHD, three coffees, and a PhD in research methods sits quietly) while my team ran a facilitated session with a disability organisation I adore.
It was a beautiful session. One of those rare rooms where people genuinely care, think deeply, and do the very best with the tools they have.
We were mapping intake and onboarding jobs – The steep, breathless, slightly-panicky-but-very-dedicated sprint a service does when welcoming a new participant into SIL, school, employment, community programs… anything. Someone in the group called it octopus work: Eight metaphorical arms gathering information, soothing families, chasing paperwork, holding risk, answering phones and not-so-silently praying the printer behaves.
But the part that struck me wasn’t tracking the contortions performed by the care team to capture all the information the organisation needed…
It was the moment someone said, “…and of course the learning never stops.”
Because it doesn’t. No matter how beautifully an organisation does intake, you never “finish” knowing a human. Not in 48 hours, 48 days, or 48 months. People don’t slot neatly into categories after Day 3 and stay there forever. They change, grow, decline, fluctuate, surprise us, delight us, confuse us and occasionally take up entirely new hobbies at 3am. Humans are beautifully inconvenient like that.
Yet most of our systems still treat intake as a single moment in time.
Why Collecting Data Is Easy – But Understanding It Isn’t
In this organisation, once a participant is settled, staff can track psychosocial indicators using a QR code.
If someone notices a sign of distress or escalation, they scan, jot an observation, and contribute to a shared picture of the person’s needs over time. I LOVE this. It’s simple, elegant, and workable. It’s also a data nerd’s playground. Because collecting data is the easy part. Understanding data is… well… a different movie entirely.
This is where my inner epistemology gremlin starts muttering in the corner:
- What counts as an observation?
- Who decides if what we saw was actually what happened?
- How do we know what we think we know?
- And why does data taken at 3:47pm on a Tuesday always seem more dramatic than data taken at 10:12am on a calm Friday?
How Human Bias Shapes Data Collection
I have spent years torturing my undergraduate students, my long-suffering husband, and my unimpressed children with monologues such as… “
“Well, the validity of that conclusion is questionable…”
“But the sensitivity of your measure is too low…”
“Have you even considered the ontology of what you’re counting?”
They mock me now. At the dinner table, one of them begins a story in my voice:
“I read an article recently…” (Cue exaggerated eyebrow raise.) “…and I have some thoughts about the measurement properties.”
It’s delightful.
But here’s the thing: Humans are magnificent, caring, observant creatures… who are also biased, pattern-seeking, story-driven creatures. We don’t collect data neutrally. We collect data emotionally, socially, tiredly, distractedly, optimistically, pessimistically, and occasionally while mid-argument with a printer. This leads to some well-known villains:
1. Negativity Bias
If you see ten neutral shifts and one dramatic shift, guess which one your brain remembers? Exactly. The dramatic one. It’s why news outlets report disasters, not mildly pleasant days.
2. Confirmation Bias
Once someone believes a participant is “escalating,” every minor wobble becomes… “See! Evidence!”. Once someone believes a participant is “doing really well,” they’re inclined to minimise signs of deterioration.
3. The ‘Big Trend or Bust’ Problem
If it doesn’t look like a strong upward line on a graph, was it even worth recording? (A thousand statisticians just flinched somewhere.)
What Famous Data Failures Teach Us About Care Systems
To make my point, let’s revisit some famous moments in history where humans very confidently misinterpreted data:
NASA’s Mars Climate Orbiter
Lost because one team used metric units and another used imperial.
(In disability services, this is like measuring “behaviour incidents” but one team uses “mildly grumpy” and another uses “apocalypse imminent.”)
Blockbuster ignoring the trend toward digital streaming
Because the data they looked at said people “liked the in-store experience.”
(Equivalent: “Participants love paperwork. That’s why they don’t fill it out.”)
The Titanic’s iceberg warnings
Multiple messages received. Multiple messages… ignored.
(See also: missed early signs of psychosocial decline.)
When our interpretation system is shaky, data can lead us straight into the ocean.
What Reliable Data Looks Like in Disability Services
The QR code is a brilliant start. But raw observation is not enough. To make data trustworthy, we need:
- Reliability – if three staff saw the same thing, would they record it similarly?
- Validity – does the thing we’re measuring actually represent what we think it represents?
- Sensitivity – can it detect small but important changes?
- Specificity – does it avoid over-calling problems that aren’t actually problems?
Right now in disability services (especially in rural orgs where turnover is high, training is stretched, and octopus work is baseline) we rely heavily on the goodwill and intuition of staff.
Staff are incredible, and their human-ness is essential to providing great care. Truly. But humans were never designed to be perfect instruments. So systems matter because people deserve better tools than guesswork and good intentions.
Why Learning About People Never Really Ends
What I saw in today’s workshop was this:
Everyone intuitively knows the learning journey is continuous. Everyone wants to understand when psychosocial needs are escalating. Everyone wants to see patterns. Everyone wants to plan proactively — not stumble into crisis reactively.
But without:
- A structured way to collect observations,
- a shared language to describe them,
- a way to reduce bias
- a system that can spot subtle patterns across multiple data points
… we remain at the mercy of whichever staff member happened to be on shift when something went wrong. That’s no one’s fault. It’s simply the consequence of data that has too many opinions of its own.
How UNPLEXi Supports Better Data-Collection and Analysis
This is exactly why we’re building UNPLEXi. To take the best parts of human observation — intuitiveness, compassion, context — and combine them with:
- evidence-informed frameworks (hello ICF)
- reliable measures
- structured data
- pattern detection that doesn’t fatigue at 3pm
- tools that help staff and organisations see what they couldn’t previously see.
Because care organisations are already doing the learning. UNPLEXi simply helps you see it.
Final Thought: Humans Change. Therefore, So Must Our Systems.
If intake is a sprint, ongoing support is an ultra-marathon. You can’t run an ultra-marathon blindfolded, guided only by vibes. Today’s workshop reminded me — again — how committed our sector is. How much we want to understand people deeply. How much we care about noticing the subtle shifts that matter. The challenge is not willingness but systems.
And if you give humans the right systems, the right data, and the right way to make sense of both… They become unstoppable.
(Also: they stop crashing into metaphorical icebergs.)
A, and the UNPLEXi team.