The Problem with Assumptions: Revisiting the Dark Figure of Sexual Recidivism

What is the actual rate of sexual recidivism given the well-known fact that many crimes go unreported? This is a difficult and important problem, and in The Dark Figure of Sexual Recidivism, Nicholas Scurich and Richard S. John (2019) attempt to make progress on it by “estimate[ing] actual recidivism rates given observed rates of reoffending” (p.172). In this article, we show that the math in their probabilistic model is flawed, but more important, we demonstrate that their conclusions follow ineluctably from their empirical assumptions and the unrepresentative empirical research they cite to benchmark their calculations. Scurich and John contend that their analysis undermines what they call the “orthodoxy in academic circles” (p.172) of low sexual recidivism rates among individuals convicted of sex offenses, but we underscore that their article does not analyze data in the traditional sense; instead, it just interprets past scholarly work through the use of strong assumptions in a way that, for practitioners, is likely to be opaque and misleading (and, for us, strays into speculation, argument, or advocacy and away from objective research). Our simple calculations show that their findings are highly sensitive to their assumptions, and we conclude that courts and others should recognize Scurich and John’s work for what it is—a set of complex hypotheticals that are no more reliable than what judges and lawyers accomplish on their own by simply recognizing the basic problem that not all sex offenses are reported

Remediation programmes for practising doctors to restore patient safety: the RESTORE realist review

Background An underperforming doctor puts patient safety at risk. Remediation is an intervention intended to address underperformance and return a doctor to safe practice. Used in health-care systems all over the world, it has clear implications for both patient safety and doctor retention in the workforce. However, there is limited evidence underpinning remediation programmes, particularly a lack of knowledge as to why and how a remedial intervention may work to change a doctor’s practice. Objectives To (1) conduct a realist review of the literature to ascertain why, how, in what contexts, for whom and to what extent remediation programmes for practising doctors work to restore patient safety; and (2) provide recommendations on tailoring, implementation and design strategies to improve remediation interventions for doctors. Design A realist review of the literature underpinned by the Realist And MEta-narrative Evidence Syntheses: Evolving Standards quality and reporting standards. Data sources Searches of bibliographic databases were conducted in June 2018 using the following databases: EMBASE, MEDLINE, Cumulative Index to Nursing and Allied Health Literature, PsycINFO, Education Resources Information Center, Database of Abstracts of Reviews of Effects, Applied Social Sciences Index and Abstracts, and Health Management Information Consortium. Grey literature searches were conducted in June 2019 using the following: Google Scholar (Google Inc., Mountain View, CA, USA), OpenGrey, NHS England, North Grey Literature Collection, National Institute for Health and Care Excellence Evidence, Electronic Theses Online Service, Health Systems Evidence and Turning Research into Practice. Further relevant studies were identified via backward citation searching, searching the libraries of the core research team and through a stakeholder group. Review methods Realist review is a theory-orientated and explanatory approach to the synthesis of evidence that seeks to develop programme theories about how an intervention produces its effects. We developed a programme theory of remediation by convening a stakeholder group and undertaking a systematic search of the literature. We included all studies in the English language on the remediation of practising doctors, all study designs, all health-care settings and all outcome measures. We extracted relevant sections of text relating to the programme theory. Extracted data were then synthesised using a realist logic of analysis to identify context–mechanism–outcome configurations. Results A total of 141 records were included. Of the 141 studies included in the review, 64% related to North America and 14% were from the UK. The majority of studies (72%) were published between 2008 and 2018. A total of 33% of articles were commentaries, 30% were research papers, 25% were case studies and 12% were other types of articles. Among the research papers, 64% were quantitative, 19% were literature reviews, 14% were qualitative and 3% were mixed methods. A total of 40% of the articles were about junior doctors/residents, 31% were about practicing physicians, 17% were about a mixture of both (with some including medical students) and 12% were not applicable. A total of 40% of studies focused on remediating all areas of clinical practice, including medical knowledge, clinical skills and professionalism. A total of 27% of studies focused on professionalism only, 19% focused on knowledge and/or clinical skills and 14% did not specify. A total of 32% of studies described a remediation intervention, 16% outlined strategies for designing remediation programmes, 11% outlined remediation models and 41% were not applicable. Twenty-nine context–mechanism–outcome configurations were identified. Remediation programmes work when they develop doctors’ insight and motivation, and reinforce behaviour change. Strategies such as providing safe spaces, using advocacy to develop trust in the remediation process and carefully framing feedback create contexts in which psychological safety and professional dissonance lead to the development of insight. Involving the remediating doctor in remediation planning can provide a perceived sense of control in the process and this, alongside correcting causal attribution, goal-setting, destigmatising remediation and clarity of consequences, helps motivate doctors to change. Sustained change may be facilitated by practising new behaviours and skills and through guided reflection. Limitations Limitations were the low quality of included literature and limited number of UK-based studies. Future work Future work should use the recommendations to optimise the delivery of existing remediation programmes for doctors in the NHS. Study registration This study is registered as PROSPERO CRD42018088779. Funding This project was funded by the National Institute for Health Research (NIHR) Health Services and Delivery Research programme and will be published in full in Health Services and Delivery Research; Vol. 9, No. 11. See the NIHR Journals Library website for further project information. </jats:sec

The Problem with Assumptions: Revisiting the Dark Figure of Sexual Recidivism

What is the actual rate of sexual recidivism given the well-known fact that many crimes go unreported? This is a difficult and important problem, and in The Dark Figure of Sexual Recidivism, Nicholas Scurich and Richard S. John (2019) attempt to make progress on it by “estimate[ing] actual recidivism rates given observed rates of reoffending” (p.172). In this article, we show that the math in their probabilistic model is flawed, but more important, we demonstrate that their conclusions follow ineluctably from their empirical assumptions and the unrepresentative empirical research they cite to benchmark their calculations. Scurich and John contend that their analysis undermines what they call the “orthodoxy in academic circles” (p.172) of low sexual recidivism rates among individuals convicted of sex offenses, but we underscore that their article does not analyze data in the traditional sense; instead, it just interprets past scholarly work through the use of strong assumptions in a way that, for practitioners, is likely to be opaque and misleading (and, for us, strays into speculation, argument, or advocacy and away from objective research). Our simple calculations show that their findings are highly sensitive to their assumptions, and we conclude that courts and others should recognize Scurich and John’s work for what it is—a set of complex hypotheticals that are no more reliable than what judges and lawyers accomplish on their own by simply recognizing the basic problem that not all sex offenses are reported

The problem with assumptions: Revisiting “The dark figure of sexual recidivism”

What is the actual rate of sexual recidivism given the well‐known fact that many crimes go unreported? This is a difficult and important problem, and in “The dark figure of sexual recidivism,” Nicholas Scurich and Richard S. John (2019) attempt to make progress on it by “estimat[ing] actual recidivism rates . . . given observed rates of reoffending” (p. 171). In this article, we show that the math in their probabilistic model is flawed, but more importantly, we demonstrate that their conclusions follow ineluctably from their empirical assumptions and the unrepresentative empirical research they cite to benchmark their calculations. Scurich and John contend that their analysis undermines what they call the “orthodoxy in academic circles” (p. 173) of low sexual recidivism rates among individuals convicted of sexual offenses, but we underscore that their article does not analyze data in the traditional sense; instead, it just interprets past scholarly work through the use of strong assumptions in a way that, for practitioners, is likely to be opaque and misleading (and, for us, strays into speculation, argument, or advocacy and away from objective research). Our simple calculations show that their findings are highly sensitive to their assumptions, and we conclude that courts and others should recognize Scurich and John’s work for what it is—a set of complex hypotheticals that are no more reliable than what judges and lawyers accomplish on their own by simply recognizing the basic problem that not all sexual offenses are reported.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/168274/1/bsl2508_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/168274/2/bsl2508.pd

The Problem with Assumptions: Revisiting “The Dark Figure of Sexual Recidivism”

What is the actual rate of sexual recidivism given the well‐ known fact that many crimes go unreported? This is a difficult and important problem, and in “The dark figure of sexual recidivism,” Nicholas Scurich and Richard S. John (2019) attempt to make progress on it by “estimat[ing] actual recidivism rates . . . given observed rates of reoffending” (p. 171). In this article, we show that the math in their probabilistic model is flawed, but more importantly, we demonstrate that their conclusions follow ineluctably from their empirical assumptions and the unrepresentative empirical research they cite to benchmark their calculations. Scurich and John contend that their analysis undermines what they call the “orthodoxy in academic circles” (p. 173) of low sexual recidivism rates among individuals convicted of sexual offenses, but we underscore that their article does not analyze data in the traditional sense; instead, it just interprets past scholarly work through the use of strong assumptions in a way that, for practitioners, is likely to be opaque and misleading (and, for us, strays into speculation, argument, or advocacy and away from objective research). Our simple calculations show that their findings are highly sensitive to their assumptions, and we conclude that courts and others should recognize Scurich and John\u27s work for what it is—a set of complex hypotheticals that are no more reliable than what judges and lawyers accomplish on their own by simply recognizing the basic problem that not all sexual offenses are reported

The Problem with Assumptions: Revisiting the Dark Figure of Sexual Recidivism

What is the actual rate of sexual recidivism given the well-known fact that many crimes go unreported? This is a difficult and important problem, and in The Dark Figure of Sexual Recidivism, Nicholas Scurich and Richard S. John (2019) attempt to make progress on it by “estimate[ing] actual recidivism rates given observed rates of reoffending” (p.172). In this article, we show that the math in their probabilistic model is flawed, but more important, we demonstrate that their conclusions follow ineluctably from their empirical assumptions and the unrepresentative empirical research they cite to benchmark their calculations. Scurich and John contend that their analysis undermines what they call the “orthodoxy in academic circles” (p.172) of low sexual recidivism rates among individuals convicted of sex offenses, but we underscore that their article does not analyze data in the traditional sense; instead, it just interprets past scholarly work through the use of strong assumptions in a way that, for practitioners, is likely to be opaque and misleading (and, for us, strays into speculation, argument, or advocacy and away from objective research). Our simple calculations show that their findings are highly sensitive to their assumptions, and we conclude that courts and others should recognize Scurich and John’s work for what it is—a set of complex hypotheticals that are no more reliable than what judges and lawyers accomplish on their own by simply recognizing the basic problem that not all sex offenses are reported

Collusion in Oligopoly: An Experiment on the Effect of Numbers and Information

Despite considerable scholarly effort, no theory has provided reliable predictions of price or output in oligopoly markets. Techniques have not been found which can unravel the complex interdependencies between firms. As long as the consequence of each firm's actions depend in large measure on the unknown reactions of other firms, rational behavior (for the firm) will be difficult to define.</p