A parameter is a placeholder for an assumption. So you may have inflation as a parameter, and assume a value of 3% for that parameter.
Therefore for a stress test you are not testing your assumption of 3%, but the effect a particularly large assumption would have on the output.
While for a sensitivity test you are testing how sensitive the output is to your assumption of 3% (eg does 3.5% provide a very different output than 3%, or does it make very little difference at all). The assumptions that are sensitive are the ones that should be focussed on with more detail at the calibration stage. But as the notes say if you expect the model to be very sensitive to a particular assumption but it turns out not to be then it may be a flaw in the model which needs to be addressed.
Scenario tests are conceivable scenarios in the real world. Holding all parameters equal except for one, which is stressed, is not a realistic scenario, as many variables are correlated.
I often think of stress tests and sensitivity tests as being useful during the calibration stage, while scenario tests are more applicable to the actual capital setting stage. There is of course overlap between them (eg if a scenario test doesn't give the output that is expected then this may be a flaw in the model), but that might be a way for you to consider them.
Don't forget that when it comes to someone sitting down and building a model, they can't simply build it, apply lots of scenarios and measure the effect. They have to check their work, observe all the nuances of the model based on their calibration, explain reasons for certain outputs, quantify the effect of particular scenarios, etc. Sensitivity tests, stress tests and scenario tests are three means of doing that.