So Why Aren't Counselors Reporting n = 1 Research Designs?

There is a growing urgency for counseling to be evidence based. The n = 1 research paradigm offers counselors in the field an opportunity to evaluate their clinical casework and report this in methodologically rigorous form to their peers. Data from the past 20 years of the Journal of Counseling & Development indicate that n = 1 studies constitute just over 1% of the total articles reporting research data, and possible reasons for this are discussed, with some suggestions made for overcoming this reluctance.     

Work, Well–being and Vocational Education: The Ethical Significance of Work and Preparation for Work

David Carr's account of the nature of professional work is described and examined. It is argued that Carr's criteria for distinguishing between professional and non–professional work are not adequate. The criteria are as follows: the professions' essential role in promoting human flourishing; their contestability; their direct concern for the well–being of clients; their provision of a high degree of autonomy for practitioners. They do not mark out a qualitative difference between professions and other occupations. Carr's notion of civic necessities applies to what he describes as non–professional as well as to professional work. The role of commercial considerations in the usefulness of some kinds of work is considered and a distinction is made between technical and occupational virtues. Carr treats professional virtues as occupational virtues without appreciating that nearly all occupations have both occupational and technical virtues. A significant part of the wouthwhileness of work is bound up with practice of the relevant occupational virtues.     

Sensitivity and Strength: Effects Of Instructions on Resistance to Change

Several research laboratories have found that instructed behavior can be less sensitive to changes in contingencies than shaped behavior. The current experiment examined whether these differences in sensitivity could be related to resistance to change. Two groups of subjects, who were matched on the basis of an initial disruption assessment, were exposed to a variable-interval 30-s schedule of reinforcement with and without a disrupter. The disrupter was a video presentation of a popular television situation comedy. One group received minimal instructions (MI) that told them only that they could earn points exchangeable for money. Each member of the second group received a complete instruction (CI) that described the topography of the target response that was yoked to a MI subject’s stable baseline response rate. The response rates under the disruption condition for the CI subjects were more resistant to change than the MI subjects in 14 out of 15 disruption sessions. These findings are discussed in terms of resistance to change being increased by instructional conditions like those manipulated and that the procedures used to test disruption provide an additional method to !waluate differences between instructed and contingency-governed behavior.

     

Using algorithms to address trade-offs inherent in predicting recidivism

Although risk assessment has increasingly been used as a tool to help reform the criminal justice system, some stakeholders are adamantly opposed to using algorithms. The principal concern is that any benefits achieved by safely reducing rates of incarceration will be offset by costs to racial justice claimed to be inherent in the algorithms themselves. But fairness trade-offs are inherent to the task of predicting recidivism, whether the prediction is made by an algorithm or human. Based on a matched sample of 67,784 Black and White federal supervisees assessed with the Post Conviction Risk Assessment, we compared how three alternative strategies for “debiasing” algorithms affect these trade-offs, using arrest for a violent crime as the criterion . These candidate algorithms all strongly predict violent reoffending (areas under the curve = 0.71–72), but vary in their association with race (r = 0.00–0.21) and shift trade-offs between balance in positive predictive value and false-positive rates. Providing algorithms with access to race (rather than omitting race or “blinding” its effects) can maximize calibration and minimize imbalanced error rates. Implications for policymakers with value preferences for efficiency versus equity are discussed.