feat(script): Add structural findings table

manuscript/article.qmd

@@ -530,6 +530,54 @@ but in turn increases overall labour force participation and employment.[^ciepli
 
 ## Structural factors
 
+{{< portrait >}}
+
+::: {#tbl-findings-structural}
+
+```{python}
+# | label: tbl-findings-structural
+from src.model import validity
+from src.model.validity import strength_for # Careful: ruff org imports will remove
+
+findings_structural = pd.read_csv(f"{g.SUPPLEMENTARY_DATA}/findings-structural.csv")
+fd_df = validity.add_to_findings(findings_structural, df_by_intervention)
+
+outp = Markdown(
+    tabulate(
+        fd_df[
+            [
+                "area of policy",
+                "internal_validity",
+                "external_validity",
+                "findings",
+                "channels",
+            ]
+        ].fillna(""),
+        showindex=False,
+        headers=[
+            "area of policy",
+            "internal strength",
+            "external strength",
+            "main findings",
+            "channels",
+        ],
+        tablefmt="grid",
+    )
+)
+del findings_structural, fd_df
+outp # type: ignore[ReportUnusedExpression]
+```
+
+Note: Each main finding is presented with an internal strength of evidence and an external strength of evidence which describe the combined validities of the evidence base for the respective finding.
+Validities are segmented to a weak (-) evidence base under a validity ranking of `{python} strength_for(r"\+")`,
+evidential (+) from `{python} strength_for(r"\+")` and under `{python} strength_for(r"\++")` and strong evidence base (++) for `{python} strength_for(r"\++")` and above.
+
+Summary of main findings for structural policies
+
+:::
+
+{{< landscape >}}
+
 ## Agency factors
 
 # Robustness of evidence