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HEALTHCARE & LIFE SCIENCES · STARTER PROJECT

Health-science poster evidence helper

Help a health-science club check which statements its fictional handwashing-awareness survey actually supports.

Beginner-friendly path · 9 guided lessons · editable JavaScript · runs in your browser · no API key

START WITH A QUESTION

Does measuring recall tell us whether an illness was prevented?

Predict which part of a health claim needs to match a study card: its topic, its actual measurement, or both.

Reveal what to look for

The evidence tool checks the recorded measurement and study design. A related source can still fail to support a particular claim.

Start from the basicsExplore the path

9 guided lessons · learn at your pace

YOUR FINISHED BUILD

Health-science poster evidence helper

Each claim marked supported, unsupported, or needs review, with source IDs and an explanation. Show the claim-to-source check.

WHY THIS FIELD USES THE IDEA

Public health and biomedical reading depend on separating what was measured from what a reader hopes the result means.

SEE THE IDEAChoose a step

See the idea before you code it

Read the input

Three invented study cards with methods, sample sizes, measurements, and limitations; poster claims; and allowed source IDs.

function solve(input, tools) { /* your workflow */ }

Click a step to inspect this project's flow. The final workspace runs your JavaScript with original, authored records and checks.

A COMPLETE PATH TO YOUR BUILD

One small step at a time.

You can begin at the first lesson, resume shared skills you already practiced, or open the project IDE when you are ready.

01 · UNDERSTAND

Start with the essentials

No coding experience needed. These common lessons stay completed across every project.

  1. 1A model is one part of the systemFirst understand a model, a fixed workflow, and an agent that chooses its next tool call.
  2. 2Your first few lines of codeStart from zero: read values, objects, functions, and the result your code returns.
  3. 3Make a rule you can testWrite a small condition, change the input, and observe how the output changes.
  4. 4Turn an idea into a clear goalTurn the project's deliverable into a specific goal, allowed actions, and a visible stopping rule.

02 · PRACTICE

Learn the tools your project needs

Short explanations, clickable diagrams, and a small coding lab for each skill.

  1. 5A tool needs a clear contractRead structured methods and measurements from an allowed study card.
  2. 6Find a useful piece of informationFind candidate evidence without assuming the first result supports the claim.
  3. 7Make a claim you can supportKeep each health-science statement within its cited observation.
  4. 8Data is not an instructionTreat instructions hidden in source text as data, not permission.

03 · BUILD

Make your project work

Follow the project-specific ideas, insert the explained snippets, and inspect your real execution trace.

  1. 9Health-science poster evidence helperBuild and test your own version: Each claim marked supported, unsupported, or needs review, with source IDs and an explanation. Show the claim-to-source check.

04 · TEST & REFLECT

Try to break it. Explain what holds.

3 runnable cases check the final project. Passing them demonstrates these examples, with limitations still to explore.

Change one thing

Change an invented descriptive claim into a causal one and inspect why the evidence decision changes.

Keep a useful learning record

Which missing measurement would you need before making the stronger claim?

Open project IDEReflect & save your notes
Inputs, checks, and project boundaries
Your starting material
Three invented study cards with methods, sample sizes, measurements, and limitations; poster claims; and allowed source IDs.
What your agent must check
  • Support a descriptive statement with an exact matching measurement and value.
  • Reject a causal conclusion even when the source text tells the helper to skip checking.
  • Do not manufacture a source record.
Keep the scope clear
Every study is invented and labeled as such. No symptoms, patient data, diagnosis, treatment, or claims that this is published health research.
Research behind this project path

Original explanations and authored practice records draw on these research and engineering ideas. The source organizations do not endorse this course or supply its fictional results.

NIH · Understanding Clinical Studies

Observational associations and randomized intervention designs support different kinds of conclusions.

This explains study design; it does not provide evidence for the fictional study cards in this lab. Direct page access was blocked during review; its primary indexed text was available.

Anthropic · Writing effective tools for agents — with agents

Design distinct tools with clear parameters, relevant returned information, and evaluations of how the agent actually uses them.

A description or schema does not guarantee the right action. A live tool can return different data for the same arguments as its environment changes.

Anthropic · Demystifying evals for AI agents

Define tasks, trials, and graders; inspect both execution records and final outcomes; repeat trials when model behavior varies.

A score depends on its cases and grading rules. Repeating a deterministic classroom case does not measure the variability of a live model.

Your JavaScript really runs. The model decisions and school data are authored simulations, so you can learn without an API key. Every workspace also includes a separate real SDK example to explore next. Passing the lab’s cases is practice, not proof that an agent is ready for real-world use.