Commitment devices to reduce violence against health care staff
Last registered on February 19, 2020


Trial Information
General Information
Commitment devices to reduce violence against health care staff
Initial registration date
February 19, 2020
Last updated
February 19, 2020 3:03 PM EST

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Primary Investigator
ETH Zurich
Other Primary Investigator(s)
PI Affiliation
ETH Zurich
Additional Trial Information
On going
Start date
End date
Secondary IDs
Violence against health workers is an important problem worldwide and in particular in developing countries. A number of recent studies describe this issue in Karachi, Pakistan, where the problem seems to be particularly large (Baig et al. 2018; Zafar et al. 2014, Nayyer-ul-Islam & Faroog, 2014)(L. A. Baig et al., 2018). Baseline data indicates that the main source of violence against health staff is patients’ attendants and their trust in the hospital staff. We use a commitment device to tackle the issue of violence. The psychological mechanism behind the commitment device is base on cognitive dissonance theory. Which says that commitments set the stage for subsequent consistent behavior (Cialdini 2007, 67). Therefore, this research tests whether a mutual commitment from hospital staff and patients’ attendants is effective in preventing or reducing violence/aggressive behavior against health care staff.
External Link(s)
Registration Citation
Kistler, Deborah and Adina Rom. 2020. "Commitment devices to reduce violence against health care staff." AEA RCT Registry. February 19.
Experimental Details
Participants to the ER have to sign a commitment where they agree to follow the rules and procedures of the hospital.
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
Reported cases of violence
Satisfaction with treatment at ER
Trust in different types of hospital staff
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
Attendants of patients to the ER in two hospitals in Pakistan will be randomly allocated to either the commitment (treatment) or the no-commitment (control) group. In the commitment group; attendants have to read and agree (or disagree) with the following statement:

"All staff at Hayatabad Medical Complex promises to behave according to the hospital’s code of conduct, and treat all patients and their attendants with care and respect.
I understand that the staff at Hayatabad Medical Complex emergency department is working to the best of their abilities to help me and other patients and attendants. I realize that I might have to wait for my turn to be seen as doctors are busy attending to more critical patients. Keeping this in mind, I promise to not get violent, misbehave with any staff member, or cause any sort of disturbance in the ER.
If you have any complaint, please use the complaint boxes installed at various points in the ER to file your complaint. 
Do you agree to treat the hospital staff as well as other patients and their attendance respectfully? YES/NO
If you have any other comments please use this box:"

The commitment statement will be given to the attendant directly at the registration, after entering the ER.
In the no-commitment group no additional signature is needed after the regular registration of the patient.
When the patient/attendants of both the control and the treatment group leave the hospital, we conduct a survey where we ask questions regarding their stay at the ER, such as satisfaction with treatment, expectation about waiting time, acceptance of aggressive behavior, trust toward hospital staff, as well as questions with respect to the commitment device. These questions will allow us to identify whether attendants remember the commitment they gave and whether they think that other attendants signed it too.
Experimental Design Details
Not available
Randomization Method
Coin flip
Randomization Unit
Individual level
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
Sample size: planned number of observations
Sample size (or number of clusters) by treatment arms
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB Name
Using Behavioural Sciences Approach to Improve ER Conditions of Hospitals in Pakistan
IRB Approval Date
IRB Approval Number