review changes

CONTRIBUTE.md

@@ -24,7 +24,8 @@ To increase the likelihood of your pull request being accepted:
 
 - If you are making visual changes, include a screenshot of what the affected element looks like, both before and after.
 - Follow the [style guide][style].
-- Keep your change as focussed as possible. If there are multiple changes you would like to make that are not dependent upon each other, consider submitting them as separate pull requests.
+- Follow the [accessibility guidance][https://nhsd-confluence.digital.nhs.uk/pages/viewpage.action?pageId=902212969]. The most important aspects are to include alt text for images that convey meaning, and null alt text for decorative images, colour not being the only way to convey any of the meaning in your content, descriptive heading and labels, and images aren't used as text (if you have images that convey text meaning, they should be SVGs), and any links have a descriptive text, not just "click here" or "link".
+- Keep your change as focused as possible. If there are multiple changes you would like to make that are not dependent upon each other, consider submitting them as separate pull requests.
 - Write [good commit messages](http://tbaggery.com/2008/04/19/a-note-about-git-commit-messages.html).
 
 ## Contribute to NHS England Data Science Website

docs/articles/posts/20240807_annotation_tools.md

@@ -34,7 +34,7 @@ As of the time of writing, there are two NER models fully integrated within the
 Both NER models in our pipeline need to be fed a list of entities to extract. This is true for many NER models, although some like [Stanza (opens in new tab)](https://stanfordnlp.github.io/stanza/) from [Stanford NLP Group (opens in new tab)](https://stanfordnlp.github.io/) and [BERT (opens in new tab)](https://huggingface.co/docs/transformers/tasks/token_classification) token classifiers do not need an initial entity list for extraction. For our privacy tool to be effective, we want our list of entities to be representative of the real entities in the data, and not miss any important information.
 
 <figure class="inline end" markdown>
-![Cartoon of man trying to extract entities. He looks confused and frustrated. He has a speech bubble saying "Extract an entity? WHat does that mean?"](../../images/annotation_tools_blog/entity_extraction_cartoon.jpg)
+![Cartoon of man trying to extract entities. He looks confused and frustrated. He has a speech bubble saying "Extract an entity? What does that mean?"](../../images/annotation_tools_blog/entity_extraction_cartoon.jpg)
 <figcaption>Figure 1: A frustrated user trying to extract entites!. </figcaption>
 </figure>
 

docs/our_work/bed-allocation.md

@@ -6,7 +6,7 @@ origin: 'Skunkworks'
 tags: ['HOSPITAL','BAYESIAN FORECASTING','MONTE CARLO','GREEDY ALLOCATION', 'PYTHON']
 ---
 
-![Browser shwing the dashboard for Kettering General Hospital that shows the forecasting of their bed occupancy.](../images/bed-allocation.png)
+![Browser showing the dashboard for Kettering General Hospital that shows the forecasting of their bed occupancy.](../images/bed-allocation.png)
 
 Bed allocation was identified as a suitable opportunity for the AI Skunkworks programme in May 2021.
 

docs/our_work/data-linkage-hub/linkage-projects/qaf.md

@@ -10,7 +10,7 @@ Data Linkage is a business-critical process within many government organisations
 
 However, too often data linkage is seen as an exclusive software development and data engineering exercise instead of a modelling challenge, and there is not an appropriate level of quality assurance applied at the different stages of the process. This is why we have worked on the [**Quality Assurance Framework for Data Linkage**](https://nhsengland.github.io/quality-assurance-framework-for-data-linkage/), which is a tool for data linkage practitioners **to determine the necessary quality assurance levels at every stage of the data linkage process**:
 
-![Quality Assurance Framework for Data Linkage screenshot. Consists of two tables. Top table has three columns: Data Preparation (contains profiling, assessment, and enrichment), Implementation (contains techniques and tools, configuration of linkage parameters/settings, and version control), and Evaluation (contains verification and validity, quality of linkage, and speed/Computational resources). The second table has a heading of "Overall Considerations" and contains: Uncertainty management, Communication of changes, Safety, Ethic and fairness, information governance, Community engagement, Knowledge management, and Continuous improvement and maintenance. ](../../../images/qafdl_overview.png)
+![Quality Assurance Framework for Data Linkage screenshot. Consists of two tables. Top table has three columns: Data Preparation (contains profiling, assessment, and enrichment), Implementation (contains techniques and tools, configuration of linkage parameters/settings, and version control), and Evaluation (contains verification and validity, quality of linkage, and speed/Computational resources). The second table has a heading of "Overall Considerations" and contains: Uncertainty management, Communication of changes, Safety, Ethics and fairness, Information Governance, Community Engagement, Knowledge Management, and Continuous improvement and maintenance. ](../../../images/qafdl_overview.png)
 
 The required level of quality assurance varies by project and is determined by the data linker and data users. The triage questions in the framework provide a structured approach to deciding the minimum expected levels by type of project.
 

docs/our_work/nursing-placement-optimisation.md

@@ -111,7 +111,7 @@ As part of the tool, the final schedule produced is reported in several differen
 - From a ward capacity utilisation perspective, showing the placement coordinator where there is spare capacity if a placement needs to be manually reallocated
 - From a placement hours perspective, providing various summaries of hours across wards, university cohorts and both weekly and quarterly summaries for the mandatory reporting required of the Trust.
 
-![Table with the fake schedule produced, each row ios a different student, then each column is a time, and the different times are filled with the ward names each student is assigned to.](../images/Example_using_fake_data.width-1534.png)
+![Table with the fake schedule produced, each row is a different student, then each column is a time, and the different times are filled with the ward names each student is assigned to.](../images/Example_using_fake_data.width-1534.png)
 > An example schedule produced using fake data.
 
 ### Outcomes and lessons learned

docs/our_work/p11_synpathdiabetes.md

@@ -7,7 +7,7 @@ tags: ['SIMULATION']
 ---
 
 <figure markdown>
-![Image of a table that compares three types of simulation models. The first type is health economics simulation models in diabetes. Examples include UKPDS (2020), NIHR SPHR Diabetes prevention model (Squires et al., 2016), and the Day model of Diabetic retinopathy (2013). The learnings from these models are the key stages of model building, key elements to include in the pathway, and best practices for conceptualizing a pathway. The second type is simulation models in transport. Examples are bus networks (Xie, Ma et al., 2014) and developments in the field (review) (Kagho, Balac et al., 2020). The learnings include good modeling of timing, such as whether agents optimize their time leaving the house for a commute, and bottleneck and corridor modeling.The third type is evacuation models. An example is modeling evacuation using a neural network (Tkachuk, Song et al., 2018). The main learning is adapting agents to respond to their environment using a neural network.](../images/p11fig1.png)
+![Image of a table that compares three types of simulation models. The first type is health economics simulation models in diabetes. Examples include UKPDS (2020), NIHR SPHR Diabetes prevention model (Squires et al., 2016), and the Day model of Diabetic retinopathy (2013). The learnings from these models are the key stages of model building, key elements to include in the pathway, and best practices for conceptualizing a pathway. The second type is simulation models in transport. Examples are bus networks (Xie, Ma et al., 2014) and developments in the field (review) (Kagho, Balac et al., 2020). The learnings include good modeling of timing, such as whether agents optimize their time leaving the house for a commute, and bottleneck and corridor modeling. The third type is evacuation models. An example is modeling evacuation using a neural network (Tkachuk, Song et al., 2018). The main learning is adapting agents to respond to their environment using a neural network.](../images/p11fig1.png)
 <figcaption>Figure 1: Table of learning algorithms considered for the simulation intelligence layer </figcaption>
 </figure>
 

docs/our_work/swpclab.md

@@ -75,7 +75,7 @@ Stay tuned for progress updates and check out our code development on GitHub.
 
 ![](../images/swpc_complex.png)
 
-*Figure 2: Diagram of the potential to expand the tooling in a vairety of ways to increase fidelity of the generated records and include additional modalitieis.*
+*Figure 2: Diagram of the potential to expand the tooling in a vairety of ways to increase fidelity of the generated records and include additional modalities.*
 
 Output|Link
 ---|---