docs(level-factors): name SP/RW adjusters as baseline-predictor associations (#389 finding 5)

docs/models/_partials/_results_factors.qmd

@@ -221,6 +221,36 @@ if factor_summary is not None and "role" in factor_summary.columns:
         print("\n:::")
 ```
 
+```{python}
+# | echo: false
+# | output: asis
+# SP / RW baseline-timing note (#389 finding 5). Speech accuracy (deapp_c) and
+# phonological memory (erbto) are read at the child's BASELINE (t1) value so the
+# randomised contrast is not conditioned on a treatment-affected descendant (#247
+# timing); their coefficients are therefore baseline-predictor associations, not
+# contemporaneous per-wave factors. Term-gated, so it prints only where such an
+# adjuster is actually in the summary (and stays silent for models without one).
+if factor_summary is not None and "term" in factor_summary.columns:
+    _terms = set(factor_summary["term"].astype(str))
+    _baseline_adj = {
+        "gamma_deapp_c": "speech accuracy",
+        "gamma_erbto": "phonological memory (nonword repetition)",
+    }
+    _present = [lbl for t, lbl in _baseline_adj.items() if t in _terms]
+    if _present:
+        _names = " and ".join(_present)
+        _plural = len(_present) > 1
+        print(
+            f"\n_Timing of the {_names} adjuster{'s' if _plural else ''}:_ read at "
+            f"the child's **baseline (t1)** value — so the randomised contrast is not "
+            f"conditioned on a treatment-affected descendant of the intervention "
+            f"(#247 timing) — {'these are' if _plural else 'so this is'} a "
+            f"**baseline-predictor association** applied across all of a child's "
+            f"observations, **not** a contemporaneous per-wave factor. Hearing, being "
+            f"exogenous, is read contemporaneously instead."
+        )
+```
+
 ```{python}
 # | echo: false
 # | output: asis