Similarly to
@Fra, I find it effective to think of QFT as a
noisy signal analysis formalism. There has to be a data analysis component to that, which can be troublesome, but I will ignore that here. Signal analysis is about measurements, but it's realistic about real measurements never being point-like and always being distorted. We never see the world as it really is, we can only build models. We do what we can and we describe what we do as carefully as we can, but we don't complain about not being able to see the universe as it really is. In QFT, a "window function" describes how a given measurement of the signal is different from being point-like: it might be a small sphere, a large cube, or an oblate Gaussian weighted function; whatever it is, almost exactly the same role is played by a "smearing function" in QFT, often also called a "test function" (The difference is that a window function is used in convolution with the field, which convolves with the field we can't actually measure, so smearing is a little more basic, but it's the same idea.)
Instead of using ##\hat\phi(x)## for a measurement-operator-valued-distribution, aka a quantum field, use ##\hat M(x)##. A measurement operator in QFT (this is quantum mechanics, which is about measurements, right?) is then constructed as ##\hat M_f=\int\hat M(x)f(x)\mathrm{d}^4x##. If you look in older textbooks like Itzykson&Zuber, you'll find some discussion of this, but in path integral text books not so often.
With ##\hat M_f##, we can do some neat things. To begin, for a Gaussian vacuum state we can write down the smeared two-measurement vacuum expectation value as ##\langle v|\hat M_f\hat M_g|v\rangle=(f^*,g)##, where ##(f,g)## is a pre-inner product (so it can be zero even if ##f## and ##g## are non-zero). Then we can compute the probability density for the self-adjoint "##f##-measurement" ##\hat M_f^\dagger=\hat M_f## in the Gaussian vacuum state as the inverse fourier transform of $$\langle v|\exp(j\lambda\hat M_f)|v\rangle=\exp(-\lambda^2(f,f)/2),$$ which is a Gaussian with variance ##(f,f)##. We obtain $$\langle v|\delta(\hat M_f-u)|v\rangle=\frac{\exp(-u^2/(f,f)/2)}{\sqrt{2\pi(f,f)}}.$$ notice that it doesn't matter what the structure of the pre-inner product is, provided that the matrix ##(f_i,f_j)## is positive semi-definite, for whatever finite set of test functions we happen to be using, so this can be about ordinary QM as well as about QFT, but, I think, much more cleanly than you'll see most other places.
This lets us think of the vacuum state as a broadband, noisy carrier signal that we can modulate in various ways, as in this slide,
It's important to recognize that any modulated state is a global object that we only know anything about because we measure it, locally, in a way that is described by the ##f## in an ##f##-measurement. The state and measurements together is a higher-order mathematical object than just an ordinary classical field because we can modulate probability distributions. The vacuum state is definitely not like your local radio station, except that we can describe what the radio station does pretty well using only coherent modulations of the vacuum state. Note that the algebra lets us also compute what the probability distribution would be for any self-adjoint operator we can construct using ##\hat M_{f_1}, \hat M_{f_2}, ...##, but of course it all gets quite complicated.
I cover a lot of ground in that talk, but you can see
the PDF on Dropbox for a little more about quantum fields in the slides surrounding the one I've copied in here. The PDF includes a link to the video on Syracuse University's website if anyone wants to really knock themselves out. What about interacting QFTs? Look at slide 22-... for a different story than I think you will find anywhere else.
All that said, I hope you find something that works for you. I don't discuss fermion fields at all, which is definitely a hit against me.