Øyvind Lockertsen is a PhD student at the PhD programme in Health Sciences.
Trial Lecture
The trial lecture starts at 10:00 in Zoom and will be presented in Norwegian.
Title: «Risikofaktorer for vold på psykiatrisk avdeling: pasientens perspektiv og erfaringer»
Public defense
The candidate will defend his thesis at 12:15 in Zoom.
Title of the thesis: Short-term violence risk assessment in acute psychiatry - A naturalistic prospective inpatient study
Ordinary opponents
- First opponent: Anna Björkdahl, PhD, RPN, Department of Clinical Neuroscience, Centre of Psychiatric Research, Karolinska Institutet, Sweden
- Second opponent: Associate Professor Asle Makoto Sandvik, Norwegian Police University College, Norway
- Leader of the evaluation committee: Professor Liv Halvorsrud, Department of Nursing and Health Promotion, Faculty of Health Sciences, Oslo Metropolitan University, Norway
Leader of the public defense
Professor Sølvi Helseth, Vice-Dean R&D, Oslo Metropolitan University
Supervisors
- Main supervisor: Professor Sverre Varvin, Department of Nursing and Health Promotion, Faculty of Health Sciences, Oslo Metropolitan University
- Co-supervisors: Professor Solveig Karin Bø Vatnar, Molde University College and Ann Færden, Oslo University Hospital
Digital defense information
Due to limitations on physical participation as a consequence of the coronavirus pandemic, the public defense will be conducted as a webinar on the zoom digital platform.
The link to the trial lecture and digital defense in Zoom will be available on the top of this page. OsloMet students and employees use OsloMet accounts. Others can download Zoom or use a browser.
How to oppose ex auditorio
Please send your question to the host during the break, before the second opponent begins. Raise your digital hand by clicking "Participants" at the bottom of the zoom window and choose "Raise Hand" if you want to voice the question yourself after both opponents have finished their questions. The technical administrator will ask to activate your microphone. Click Yes.
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Publication of the approved PhD thesis
Request a copy of the PhD thesis by e-mail. Include the name of the PhD candidate.
Abstract
Background
This doctoral thesis is based on a PhD project that was part of a larger prospective naturalistic research project: “Violence risk assessment in an acute psychiatric ward”. Violence within the health sector is a global and increasing concern. Aggression and violence occurs frequently in mental healthcare settings and is perpetrated by both males and females. Current violence risk assessment methods seem to have reached an upper limit of accuracy. A broader approach to risk assessments is suggested, hence different perspectives may provide a deeper and improved understanding of patients’ violence risk. Risk assessments in acute psychiatric settings must also be performed quickly, and predictions must be accurate over the short-term.
Aim
The overall aim of this doctoral thesis was to investigate an extended model for shortterm violence risk assessments in acute psychiatric units. The thesis investigated patients’ self-assessments of risk (SRS) as a risk marker for inpatient violence (Study 1), the association between short-term risk assessments with the Brøset Violence Checklist (BVC) and imminent violence throughout hospitalisation (Study 2), and whether an extended model combining BVC, SRS and single items from Violence Risk Screening 10 (V-RISK-10) provides improved short-term predictive accuracy compared to the BVC alone (Study 3).
Methods
The target population was all patients involuntarily and voluntarily admitted to an acute psychiatric ward in Oslo over one year between March 21, 2012, and March 20, 2013 (N = 558). Thirty patients withdrew from participation, resulting in an initial study sample of 528 patients from 717 admissions. In Study 1, logistic regression was conducted to investigate the predictive accuracy of SRS. Interaction analysis was used to investigate gender differences. In Study 2, logistic regression and generalised linear mixed model (GLMM) analyses were conducted to investigate the predictive accuracy of the BVC. Interaction analyses were conducted to investigate possible gender differences. In Study 3, stepwise multivariate GLMM analyses were conducted: (i) BVC, (ii) BVC + SRS. (iii) BVC + SRS + single items from V-RISK-10
Findings
Patients who reported their risk of violence to be either moderate, high, don’t know or refused to answer (positive predictor of SRS) were more than four and a half times more likely to be violent, compared with those who reported no or low risk. Interaction analysis showed significant gender differences with SRS as a stronger risk predictor for women (Study 1). Findings confirm that the BVC is a suitable short-term risk assessment instrument throughout hospitalisation in acute psychiatric units, also when differentiating threats and physical violence, differentiating males and females and adjusted for diagnostic subpopulations and circadian variability. Interaction analyses displayed no significant differences in the association between males and females (Study 2). When adjusting for repeated measurements, an extended model for short-term risk assessment, consisting of BVC, SRS and Item 2 Previous and/or current threats in V-RISK-10 explained more of the imminent violence, compared to the BVC alone (Study 3).
Conclusions
This is the first attempt to investigate an extended model for short-term risk assessments by including inpatients’ own risk assessments as part of the model. Inpatients’ self-assessments may contribute as a risk marker for inpatient violence. The findings confirm the predictive validity of BVC when adjusted for repeated measurements. Findings also indicate that an extended model for short-term risk assessments explains more variance of imminent violence than short-term risk assessments with the BVC alone. Nonetheless, the results must be interpreted with caution, and a number of limitations should be borne in mind.
Questions?
Who can answer questions prior to trial lecture and public defense? phd-hv@oslomet.no
