USING Q-METHODOLGY TO EXPLORE PRESCRIBING DECISION MAKING - A PROMISING APPROACH?

Purpose:   The presentation will explore the background and uses of Q-methodology as a research approach for understanding decision making in the clinical setting.

Method(s):   Q-methodology is a research method that seeks to understand subjective experience and beliefs.  Subjects are asked to rank statements (the Q-sample) along a quasi-normal grid, in which either extreme represents opposites on a single face-valid dimension (e.g. disagree-agree/unimportant-important) in a process known as a Q-sort.  As study participants sort the Q-sample, patterns  (shared views of the phenomenon being studied) start to emerge that can be analysed and interpreted using factor analysis.

Result(s):   The presentation will be illustrated using examples drawn from an on-going study of nurses working in acute care settings in one Scottish Health Board, and their decision making around prescribing practice.  Prescribing is a new area of practice for clinically experienced nurses with additional training within the United Kingdom healthcare environment.  Little is known about patterns of prescribing by this group of practitioners in the secondary care setting, but a preliminary study by the author showed variations in practice that could not be explained by clinical caseload. A further study to investigate the cause of these variations is being undertaken; Q-methodology has been used to understand the factors (including intrinsic clinician factors, and extrinsic patient, drug, or environmental factors) that influence prescribing decisions in three clinical scenarios - general prescribing, antimicrobial prescribing for presumed infection, and prescribing for patients in pain.  This study will be used to illustrate the development of the Q-sample from a literature based concourse, the ranking of influencing factors in the Q-sort, and the prelimanary analysis and interpretation of these data.

Conclusion(s):   Q-methodology is a research approach that allows robust collection and analysis of subjective data from a variety of subjects, including clinicians, patients, and health service manaagers.  It has advantages over other methods in the speed and acceptability of the data collection process, and the much smaller sample size required for meaningful interpretation.  This study demonstrates its use in understanding the factors that drive previously unobserved and unexplained variations in prescribing decison making and practice in a group of nurses working in acute in-patient care settings.