Closed sayantikabanik closed 2 years ago
Gist from the recording of Abhay :
Three different aspects dimensions burnout
Exhaustion:Mental exhaustion, overwork, responsibilities at home ,work, no bandwidth left
Depersonalisation:You look at yourself as robot, detach yourself from the work
Lack of achievement:Your level of achievement is less
Our goal is to find many different ways to find where people are on this spectrum(like may be you are very exhausted but other facets are fine)
How they combine make different profiles .
Our goal is to assess where people are by measuring these aspects.
Early efforts:Measurement and quantification.
Based on the underlying foundation we should be able to tell the state of workforce to the manager Most powerful state to summarise this data for the managers to understand.
2.Phase2:
3.)Slice and dice: Manager of the group wants to know where the problems are happening, example same job function form different locations or different combinations of the Data.More like a comparator One for the employee and one for the HR Employee-Strength finder:Do I have others who are like me Heatmap kind of thing how others are feeling ad reporting the same and relevant to the employee
Hard core machine learning:Machine learning model which takes a review and predict either the overall score or the category We have the labelled data.
Going through the HBR article. Will jot my points here.
I started EDA on the CSV files. I shall post my analysis, relevant documents, python notebooks
The HBR article mostly talks about the signs of burnout, the stressors in a job leading to burnout, how one can combat/prevent burnout and how leaders(and organizations) can help to solve this issue by putting sustainable measures in place.
Only a small(last) chapter is dedicated on measurement of burnout.
The suggestions highlighted can be a good consideration when we are building the suggestion box.
I have written this summary chapter-wise so that it will be easy to go back for any references.
Google drive link: https://docs.google.com/document/d/1CAzxbs6sSyOU1RZSSkbwkPIXyOVcua3B/edit?usp=sharing&ouid=100584766015578644570&rtpof=true&sd=true
I will look into other articles around how burnout is measured and dig further into Maslach Burnout Inventory (MBI)
Update on my progress so far:
Created a Jupyter Notebook reviewsCombined.ipynb to combine all given datasets (HCl, TCS, Wipro, Infosys) into a single csv file
Started EDA on entire data. Updated Data Cleaning.ipynb
Below are few key observations and relevant progress:
Next Steps: Need to work on figuring out below items:
Sayantika