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A model to explain road traffic data collection and registry in Iran: a grounded theory
Sakineh Sharifian1, Reza Khani Jazani2, Davoud Khorasani-Zavareh3
1Department of Nursing Management, School of Nursing and Midwifery, Iran University of Medical Sciences, Tehran, Iran.
Background:
Sufficient data should be gathered and analyzed to increase awareness and attention of the community and policymakers in the field of road traffic injury (RTI) prevention. While various organizations and stakeholders are involved in road traffic crashes, there is no clear lead agency for data collection system in RTIs. Exploring stakeholders' perspective is one of the key sources for understanding this system. The purpose of this study is to identify the process of RTI data collection system based on stakeholders' experience.
Methods:
This qualitative study was conducted employing grounded theory approach since September 2017 to December 2018 in Iran. Participants in this study were the authorities of the Emergency organizations, police, Ministry of Health and Medical Education, faculty members, as well as executive staff and road users who were involved in collecting and recording data (n=15). Data collection was carried out through face-to-face interviews using purposeful and theoretical sampling. Data analysis was performed based on Strauss and Corbin 2008.
Results:
The core category was identified as "separated registration" explaining the process of collecting and recording road traffic injury data. Other variables obtained using the Strauss and Corbin Paradigm model were categorized as context, casual, intervening, strategies, and outcomes factors. The findings were classified into five groups including lack of trust in road safety promotion, process factors, management and organizational factors, failure of quality assurance, and administrative and organizational culture.
Conclusions:
The most important theory is "separated registration" and non-systematic registry system of road traffic injury data which is shown in a conceptual model. The findings of this study will help policymakers for better understanding the collecting and recording of RTI information.
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