Fix build issue

Author: JackWilb

blog/2026-02-23-release-2.4.md

@@ -7,6 +7,8 @@ authors:
 
 Version 2.4 of reVISit is out – and for once it doesn’t pack a lot of new features. Rather, it’s all about **quality of life**: we fixed bugs, improved stability, and, most importantly, made the documentation much much better! 
 
+<!-- truncate -->
+
 We also added a few new things here and there, but they’re just quality of life improvements for supporting studies currently under development by the community.
 
 ### Should I Update Now?

docs/designing-studies/sequences/dynamic-blocks.md

@@ -4,7 +4,7 @@ Some studies require knowledge of the participant's responses to previous questi
 
 ## Overview
 
-In reVISit, this can be achieved using dynamic blocks. Dynamic blocks are blocks that use a function to calculate what the next component will be. See the [DynamicBlock](../typedoc/interfaces/DynamicBlock.md) for the complete interface definition.
+In reVISit, this can be achieved using dynamic blocks. Dynamic blocks are blocks that use a function to calculate what the next component will be. See the [DynamicBlock](../../../typedoc/interfaces/DynamicBlock/) for the complete interface definition.
 
 The dynamic block function is a TypeScript/JavaScript function that is defined in the `src/public/study-name/` folder and is referenced in the study configuration file like so:
 

docs/designing-studies/sequences/study-sequences.md

@@ -102,11 +102,11 @@ Studies frequently want portions of their trials to be random, but also want to
 This option will create a latin square for any block using one behind the scenes, iterate through the latin square as new participants request sequences, and refill it when empty.
 
 :::warning
-The Latin square theoretically ensures proper balance between conditions, however the balance is only guaranteed if all participants complete the study, which is frequently not the case in online studies. If a participant doesn't complete the study and should be discarded, that “draw” from the latin square is used up, which could lead to inconsistencies (e.g., if more participants don't complete one condition of the study more often than others). You can counter that effect by `rejecting` participants via the [data dashboard](../../analysis/study-summary/). When a participant is rejected, their data gets flagged (but not deleted), and the sequence they had is returned to the sequence pool. This ensures that participants that start a study but do not complete it or are stopped before completing it (failed attention check, refused consent form, etc.) do not use up a row of any generated latin squares.
+The Latin square theoretically ensures proper balance between conditions, however the balance is only guaranteed if all participants complete the study, which is frequently not the case in online studies. If a participant doesn't complete the study and should be discarded, that “draw” from the latin square is used up, which could lead to inconsistencies (e.g., if more participants don't complete one condition of the study more often than others). You can counter that effect by `rejecting` participants via the [data dashboard](../../../analysis/study-summary/). When a participant is rejected, their data gets flagged (but not deleted), and the sequence they had is returned to the sequence pool. This ensures that participants that start a study but do not complete it or are stopped before completing it (failed attention check, refused consent form, etc.) do not use up a row of any generated latin squares.
 
 In practice, it is useful to recruit participant in batches and reject incomplete participants before starting a new batch, to ensure that the latin square remains balanced. 
 
-For other ways to ensure balance in your study design, please review this [FAQ item](../faq.md#q-how-can-i-ensure-balanced-numbers-of-participants-between-conditions-in-my-study-design) and consider using [URL-based conditions](../url-conditions/).
+For other ways to ensure balance in your study design, please review this [FAQ item](../../../faq/#q-how-can-i-ensure-balanced-numbers-of-participants-between-conditions-in-my-study-design) and consider using [URL-based conditions](../url-conditions/).
 :::
 
 ## Showing a Subset of All Trials
@@ -373,4 +373,4 @@ import StructuredLinks from '@site/src/components/StructuredLinks/StructuredLink
     {name: "DeterministicInterruption", url: "../../typedoc/interfaces/DeterministicInterruption/"},
     {name: "SkipConditions", url: "../../typedoc/type-aliases/SkipConditions/"}
   ]}
-/>
+/>

docs/faq.md

@@ -65,7 +65,6 @@ A: ReVISit has built-in error handling so your study can still run even if somet
 
 A: There are several strategies to ensure balanced participant numbers between conditions in your study design using reVISit:
 1. **Assigning participants to conditions via URL parameters**: You can assign participants to specific conditions via URL parameters using [study conditions](../designing-studies/sequences/url-conditions/). This enables you to create custom recruitment links for each condition, making it easier to control participant distribution.
-1. **Latin square design**: As described in the [Study Sequences documentation](../designing-studies/study-sequences/#latin-square), you can use Latin square sequences to systematically vary the order of conditions across participants. This helps ensure that each condition appears in each position equally often. However, be aware that if participants drop out or are rejected, this can affect the balance (see the documentation of [latin squares](../designing-studies/study-sequences/#latin-square) for more details).
+1. **Latin square design**: As described in the [Study Sequences documentation](../designing-studies/sequences/study-sequences/#latin-square), you can use Latin square sequences to systematically vary the order of conditions across participants. This helps ensure that each condition appears in each position equally often. However, be aware that if participants drop out or are rejected, this can affect the balance (see the documentation of [latin squares](../designing-studies/sequences/study-sequences/#latin-square) for more details).
 1. **Random assignments**: If you are recruiting a large number of participants, random assignment to conditions can help achieve balance over time. While this method may not guarantee perfect balance at any given moment, it tends to even out as more participants are added. This is a viable option when strict balancing is less critical.
 1. **Setting up separate studies**: When precise control is needed, consider setting up separate studies for each condition. This allows you to monitor and control the number of participants in each condition directly. However, this comes at the cost of increased administrative overhead (e.g., separate recruitment links, separate datasets, separate specifications).
-