Siegert's Effort-Reward Imbalance (ERI) model proposes that when there is an imbalance between work effort and reward —particularly with effort being greater than the reward— work stress results. This can lead to a variety of adverse health outcomes. The model also suggests that over-commitment, or a personal motivation to work excessively, can increase the risk of these adverse health outcomes. ERI is measured through a questionnaire that includes items concerning Effort, Reward, and Over-Commitment. The questionnaire has long and short versions, both of which can use four-point or five point Likert scales.
The ERI questionnaire is a standardized, self-report measure of ERI. The long version has 16 items: 10 measuring reward, six measuring effort, and six measuring over-commitment. To identify ERI, the effort-reward ratio is calculated. ERI is present when ER ≠ 1, with ER <1 indicating an imbalance in favor of rewards and ER >1 indicating an imbalance in favor of effort.
Results of the ERI battery are then coded to allow calculation of an Effort-Reward Index based on the formula: ER = k(E/R) (where E and R are the effort and reward scores, and k is a correction factor (k = 7/3 for the short version, and k = 10/6 for the long version)). Effort-Reward Imbalance (ERI) is present when ER ≠ 1, with ER <1 indicating an imbalance in favor of rewards and ER >1 indicating an imbalance in favor of effort.
This project will parse the results of a Survey Monkey administered questionnaire based on the 5 point scale version of the ERI instrument. Responses will be opened with file_handler.py, recoded with answer_recoder.py, and the ERI calculated with index_calculator.py.
In addition, freetext answers will be read by a GPT as instructed by sentiment_analysis.py. Relevent quotes will be found by GPT as instructed by quote_finder.py; which work will be double-checked by a different GPT instance as instructed by hallucination_checker.py