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50 change management and git#74

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50-change-management-and-git
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50 change management and git#74
aassuied-ps wants to merge 8 commits into
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50-change-management-and-git

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@aassuied-ps

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Draft PR. Currently, it's just an introduction and titles for topics I think we could work on.

@aassuied-ps aassuied-ps linked an issue Jun 3, 2026 that may be closed by this pull request
@kieranjmartin

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@aassuied-ps I added some content in the suggestions, thanks for starting this!

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Only reviewed first two sections so far

This chapter will discuss challenges in adoption of Git as well as how these might be addressed.
## Introduction

Statistical programmers in the pharmaceutical industry operate in a highly regulated environment where validated, reproducible analysis is paramount. For several years, the standard workflow has revolved around statistical computing environments managed through shared network drives, strict naming conventions, and manual version control practices (saving files with incremental version numbers or descriptive suffixes before archiving) or with regular server backups. These habits, while informal, have been deeply embedded in day-to-day practice and have served as the de facto audit trail in many organizations.

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Statistical programmers in the pharmaceutical industry operate in a highly regulated environment where validated, reproducible analysis is paramount. For several years, the standard workflow has revolved around statistical computing environments managed through shared network drives, strict naming conventions, and manual version control practices (saving files with incremental version numbers or descriptive suffixes before archiving) or with regular server backups. These habits, while informal, have been deeply embedded in day-to-day practice and have served as the de facto audit trail in many organizations.
Statistical programmers in the pharmaceutical industry operate in a highly regulated environment where validated, reproducible analysis is paramount. Historically, the standard workflow revolved around statistical computing environments managed through shared network drives, strict naming conventions, and manual version control practices (saving files with incremental version numbers or descriptive suffixes before archiving) or with regular server backups. These habits, while informal, have been deeply embedded in day-to-day practice and have served as the de facto audit trail in many organizations.


Statistical programmers in the pharmaceutical industry operate in a highly regulated environment where validated, reproducible analysis is paramount. For several years, the standard workflow has revolved around statistical computing environments managed through shared network drives, strict naming conventions, and manual version control practices (saving files with incremental version numbers or descriptive suffixes before archiving) or with regular server backups. These habits, while informal, have been deeply embedded in day-to-day practice and have served as the de facto audit trail in many organizations.

Introducing Git into this context presents a unique set of challenges that go beyond mere tool adoption. It relies on notions such as sub-branches linked to a main branch, commits, remote repositories and merges, that can be difficult to adopt for teams used to work in other environments. The learning curve can be difficult and requires to embrace a more collaborative and transparent workflow.

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Introducing Git into this context presents a unique set of challenges that go beyond mere tool adoption. It relies on notions such as sub-branches linked to a main branch, commits, remote repositories and merges, that can be difficult to adopt for teams used to work in other environments. The learning curve can be difficult and requires to embrace a more collaborative and transparent workflow.
Introducing Git into this context presents a unique set of challenges that go beyond mere tool adoption. It relies on notions such as sub-branches linked to a main branch, commits, remote repositories and merges, that can be difficult to adopt for teams used to working in other environments. The learning curve can be difficult and requires embracing a more collaborative and transparent workflow.


Introducing Git into this context presents a unique set of challenges that go beyond mere tool adoption. It relies on notions such as sub-branches linked to a main branch, commits, remote repositories and merges, that can be difficult to adopt for teams used to work in other environments. The learning curve can be difficult and requires to embrace a more collaborative and transparent workflow.

Furthermore, the regulatory framework of the pharmaceutical industry is strict. Any change management system touching analysis code or submission-relevant outputs must be set in a GxP environment. Git must be used in a way that ensures theses compliances (traceability, audit, etc).

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Furthermore, the regulatory framework of the pharmaceutical industry is strict. Any change management system touching analysis code or submission-relevant outputs must be set in a GxP environment. Git must be used in a way that ensures theses compliances (traceability, audit, etc).
Furthermore, the regulatory framework of the pharmaceutical industry is strict. Any change management system touching analysis code or submission-relevant outputs must be set in a GxP environment. Git must be used in a way that ensures compliance (traceability, audit, etc).


Furthermore, the regulatory framework of the pharmaceutical industry is strict. Any change management system touching analysis code or submission-relevant outputs must be set in a GxP environment. Git must be used in a way that ensures theses compliances (traceability, audit, etc).

Organizational culture also plays a significant role. Statistical programmers often work independently or in small teams with well-established personal workflows. Moving to a Git focused workflow requires not only technical training, but to being open to new mindsets and ways of working.

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Organizational culture also plays a significant role. Statistical programmers often work independently or in small teams with well-established personal workflows. Moving to a Git focused workflow requires not only technical training, but to being open to new mindsets and ways of working.
Organizational culture also plays a significant role. Statistical programmers often work independently or in small teams with well-established personal workflows. Moving to a Git-focused workflow requires not only technical training, but to being open to new mindsets and ways of working.


## The Change Curve

The Change Curve is a well known model for understanding how employees resond to change @HOLDSWORTH2024254. While there are many different implementations of it, generally speaking it is understood to be split into two main phases, first of resistance and then of acceptance. An important attitude in any company trying to make any sort of change, be it organisational or technological, is to have empathy for those individuals who are experiencing it, and give them the support they need.

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I feel that it is likely better to use more than just two phases to describe the curve. Even better is if we can add an image depicting the curve.

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The Change Curve is a well known model for understanding how employees resond to change @HOLDSWORTH2024254. While there are many different implementations of it, generally speaking it is understood to be split into two main phases, first of resistance and then of acceptance. An important attitude in any company trying to make any sort of change, be it organisational or technological, is to have empathy for those individuals who are experiencing it, and give them the support they need.
The Change Curve is a well known model for understanding how employees respond to change @HOLDSWORTH2024254. While it has many different forms, it generally begins with initial shock, followed by resistance, low morale, gradual experimentation, and then acceptance and integration. An important consideration for any company trying to make any significant change, be it organisational or technological, is to have empathy for those individuals who are experiencing it and give them the support they need.


The Change Curve is a well known model for understanding how employees resond to change @HOLDSWORTH2024254. While there are many different implementations of it, generally speaking it is understood to be split into two main phases, first of resistance and then of acceptance. An important attitude in any company trying to make any sort of change, be it organisational or technological, is to have empathy for those individuals who are experiencing it, and give them the support they need.

We must understand that any change will take time to embed into an organisation, and the main thing we can influence is how fast that change goes, and how well we can adopt the technology.

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"and how well we can adopt the technology"--what does this mean?


We must understand that any change will take time to embed into an organisation, and the main thing we can influence is how fast that change goes, and how well we can adopt the technology.

In this chapter we will mostly ignore the how of using Git within your company (for more, please see the [recommendations](recommendations.qmd) chapter), and look instead about the things you can do to support your team as they learn to adopt Git in practice.

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In this chapter we will mostly ignore the how of using Git within your company (for more, please see the [recommendations](recommendations.qmd) chapter), and look instead about the things you can do to support your team as they learn to adopt Git in practice.
In this chapter, we will largely set aside the specifics of how Git is used within your organization (for more, please see the [recommendations](recommendations.qmd) chapter). Instead, we will focus on the ways you can support your team as they learn to adopt Git in their day-to-day work.

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Change management and git

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