Stigma Toward Individuals Engaged in Consensual Nonmonogamy: Robust and Worthy of Additional Research

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/102134/1/asap12020.pd

Deception in context: coding nonverbal cues, situational variables and risk of detection

There are many situations in which deception may arise and understanding the behaviors associated with it are compounded by various contexts in which it may occur. This paper sets out a coding protocol for identifying cues to deception and reports on three studies, in which deception was studied in different contexts. The contexts involved manipulating risks (i.e., probability) of being detected and reconnaissance, both of which are related to terrorist activities. Two of the studies examined the impact of changing the risks of deception detection, whilst the third investigated increased cognitive demand of duplex deception tasks including reconnaissance and deception. In all three studies, cues to deception were analyzed in relation to observable body movements and subjective impressions given by participants. In general, the results indicate a pattern of hand movement reduction by deceivers, and suggest the notion that raising the risk of detection influences deceivers? behaviors. Participants in the higher risk condition displayed increased negative affect (found in deceivers) and tension (found in both deceivers and truth-tellers) than those in lower risk conditions

The effect of cognitive load on faking interrogative suggestibility on the Gudjonsson Suggestibility Scale

In the light of recent studies into the impact of cognitive load on detecting deception, the impact of cognitive load on faking on the Gudjonsson Suggestibility Scale (GSS) was investigated. Eighty undergraduate students participated in the study, and were randomly assigned to one of four conditions resulting from a combination of the factors: instruction type (genuine or instructed faking, see Hansen, Smeets, & Jelicic, 2009) and concurrent task (yes or no). Findings show that instructed fakers, not performing a concurrent task, score significantly higher on yield 1 in comparison to genuine interviewees. This is in line with previous studies into faking on the GSS. However, instructed fakers, performing a concurrent task, achieved significantly lower yield 1 scores than instructed fakers not performing a concurrent task. Genuine (non fakers) showed a different response to increased cognitive load during the dual-task paradigm. This study suggests that increasing cognitive load may potentially indicate (and preclude) faking attempts on the yield dimension of the Gudjonsson Suggestibility Scale

No Shelter for Singles: The Perceived Legitimacy of Marital Status Discrimination

Providing the first empirical evidence of discrimination against singles, participants in multiple experiments favored married couples over various types of singles and failed to recognize such differential treatment as discrimination. In four experiments, undergraduates and rental agents read descriptions of multiple applicants for a rental property and chose one. The applicant pool, varying across experiments, included a married couple and different types of singles. Although the applicants were similar on substantive dimensions, participants consistently chose the married couple over the singles and explicitly stated that the applicants' marital status influenced their choice. In Experiment 5, participants read examples of housing discrimination against singles and other more recognized stigmatized groups. Participants rated discrimination against singles as more legitimate than discrimination against virtually all of the other groups

Lie experts' beliefs about non-verbal indicators of deception

ABSTRACT.. Beliefs about behavioral clues to deception were investigated in 212 people, consisting of prisoners, police detectives, patrol police officers, prison guards, customs officers, and college students. Previous studies, mainly conducted with college students as subjects, showed that people have some incorrect beliefs about behavioral clues to deception. It was hypothesized that prisoners would have the best notion about clues of deception, due to the fact that they receive the most adequate feedback about successful deception strategies. The results supported this hypothesis

A formal account of dishonesty

International audienceThis paper provides formal accounts of dishonest attitudes of agents. We introduce a propositional multi-modal logic that can represent an agent's belief and intention as well as communication between agents. Using the language, we formulate different categories of dishonesty. We first provide two different definitions of lies and provide their logical properties. We then consider an incentive behind the act of lying and introduce lying with objectives. We subsequently define bullshit, withholding information and half-truths, and analyze their formal properties. We compare different categories of dishonesty in a systematic manner, and examine their connection to deception. We also propose maxims for dishonest communication that agents should ideally try to satisfy

Helping Understand Sleep Heals-ICU Alarm Counts and Richard-Campbell Sleep Questionnaire

Abstract This program was named the HUSH project and is funded by the American Association of Critical Care Nurses. H.U.S.H. has come to stand for Helping Understand Sleep Heals . The purpose of the current study was to evaluate the noise levels of the various alarms in the ICU and to determine if noise levels had an impact on the quality of sleep the patient received at night. This project looked at 260 patients out of a total of 430 patients in the ICU with an average age of 67 years old in a 20-bed Intensive Care Unit at Lehigh Valley Hospital-Muhlenberg. During this project, five different evaluation tools were used to measure areas of the project. These tools include: HCAPHS, manual alarm counts, decibel meter readings, Phillips Monitor Alarm Trigger printouts, and the Richard-Campbell Sleep Study. The results had no significant difference from the pre-launch and launch, except for a 16.% decrease in false alarms. Noise is still a huge issue in hospitals and is a factor in sleep, but decibel levels are still too high with alarms being the leading cause. Keywords: alarms, decibel meter readings, sleep, HUSH, noise Sleep is a time for the body to restore elements lost and recuperate after stress. For many patients in the Intensive Care Units (ICUs) at hospitals, sleep is not always obtainable in these noisy and chaotic environments. This can become an issue for the critically-ill patients that are trying to recover from a traumatic, acute episode, and it can take as little as 24 to 48 hours for the body to begin reacting negatively to a lack of sleep in patients (Dennis, Lee, Knowles Woodard, Szalaj, & Walker, 2010). In fact, one study found that patients were disturbed on average every 20 minutes, even while they were sleeping (Dennis. et al., 2010). A lack of sleep has been shown to play a part in falls, confusion, and increased medication and restraint use for patients (Mazer, 2006, p. 145). Furthermore, ICU patients are at a higher risk of developing delirium (Olson, 2012). A large reason for sleep disturbances in hospitals can be attributed to noise levels. According to the United States Environmental Protection Agency (EPA), the guidelines for background noise are 45 decibels (dβ) during the day and 35 dβ at night in patient rooms. Research has shown that hospital noise levels exceed this recommended guideline of the EPA, making sleep harder to come by in an already hectic environment (Kahn, et al., 1998). The Help Understand Sleep Heals (H.U.S.H.) project, took place in the 20-bed ICU of Lehigh Valley Hospital-Muhlenberg over a 16-month period. The project was launched at the beginning of September 2013 and will conclude at the end of July 2014. This study includes a population with an average age of 67 years old, and consists of sample 260 patients out of a total of 430 patients admitted to the ICU within that 16-month period. The purpose of the current study was to decrease the level of noise and the number of controllable alarms to help aid in increased patient and staff satisfaction. Methods The H.U.S.H. Project is a two stage project. The first stage is to evaluate the decibel levels of alarms that are used in the ICU, and the number of alarms that sound during a two hour time period. The second stage is assessing the patient\u27s perception of sleep along with the nurse\u27s perception of the patient\u27s sleep. During the week the project was first launched, the ICU at Muhlenberg introduced Quiet Time to the unit between the hours of 1 o\u27clock to 4 o\u27clock a.m., and 2 o\u27clock to 4 o\u27clock p.m. Actions taken during Quiet Time hours include: dimmed lights, visitors may be asked to leave, television volumes are reduced, headsets and ear buds may be used, staff will limit nursing activities during these hours, patient\u27s door will be closed, and therapeutic interventions will be performed in a quiet manner. During this first week, the unit eliminated hallway ventilator alarms that had the potential to average around 86 to 90 plus decibels. In the first stage, manual alarm counts were done in two hour sessions. The observer would station themselves at either the front or back monitors. Each monitoring sections received information for 12 rooms with four of the rooms overlapping between stations. The observer would use a chart, shown in Figure 1, that listed all the alarms that would be monitored. These alarms included: ventilators and BiPAP, EKGs, blood pressure, pulse ox, IV pumps, apnea, bed alarms, patient call bells, loud staff, tubing station, and other miscellaneous alarms that are too uncommon to be grouped into its own category. During the two hours period, the observer would time the duration of the alarms going off and then determine whether it was a true or false alarm. Meanwhile, there would be a SL120 decibel counter located at the monitoring station. The decibel counter would be read at the beginning and the end of the two hour period, as well as fifteen minute intervals in between. At the end of the two hour reading, the observer would access the information in the monitors and printout the Phillip Monitor Alarm Trigger Printout. This tool calculated the amount of red, yellow, and bed alarms that registered for each room. The second stage of the project includes the Richard-Campbell Sleep Study Questionnaire, and HCAPH scores. At night, the Richard-Campbell Sleep Study Questionnaire would be given to the nurse and any patient who is not on any type of ventilator, and who is awake and oriented to time, place, and person. The observer would hand out the surveys to the night nurse and their patient at eleven o\u27clock p.m. to either even or odd rooms for evaluation of the upcoming night\u27s sleep. The rooms used per night was determined by whether the date was an even or odd number. For example, if the date was the twenty-second, then all the even room numbers would be given the survey. Each survey received a code that was made up of the date, room number, and participant. For instance, the night the survey is given out is December 22, 2013, and the patient that will be receiving the survey is in room 234. The code for the matching survey would be 12221334-P. The survey code for the nurse of that same room would be 12221334-N. The Richard-Campbell Sleep Study Questionnaire is a five question survey with a sixth question being optional, shown in Figure 2. In order to answer the survey, the patient must put an X on the 100 centimeter line below each question. The placement of the X should correspond to the perception of the participant of the survey, whether that be the nurse or the patient. In order to calculate the survey into a numerical value, the observer would draw a line through the center of the X , and then measure the 100 cm line to the vertical line running though the X . On the other hand, the HCAPH scores were analyzed monthly. The section studied for the HCAPH scores was Quietness to see if the scores met the target goals for each month. Results During the project, 260 patients out of 430 patients were monitored through alarm and decibel counts for a two hour time period. Since the project finishes at the end of July, the data presented is incomplete. These results are missing the last few months of data collection. Figure 3 shows the noise levels in decibels (dβ) within the unit. It demonstrates a slight increase in the minimum, maximum, and mean from the pre-launch to the launch portions. Figure 4 and Figure 5 demonstrate the number of true and false alarms activated during pre-launch and launch portions of the project. There was a 16.8% decrease in false alarms from pre-launch to launch. Figure 6 and 7 show the eleven different alarms counted and the percent of which they occurred. No significant difference was noticed among the types of alarms. Some alarms increased slightly while others slightly decreased. Figure 8 shows the minimum, maximum, and average length of time alarms sounded within the unit. There was no significant difference noticed in the average length of time, but both the minimum and maximum lengths decreased. The minimum decreased 8.5% whereas the maximum decreased 29.9%. Figure 9 demonstrates the difference in the number of red, yellow, and bed alarms that signaled in the unit. No significant difference was noticed in the number of red alarms, even though yellow and bed alarms slightly decreased. Figure 10 and Table 1 show the HCAPHS for Quietness within the ICU along with the target score and yearly percentile. There was no significant difference though there was a slight change at the end of the project during the months from February 2014 to June 2014. Figure 11 shows the averages for the questions answered by nurses and patients from the Richard-Campbell Sleep Study Questionnaire. No significant difference was observed between nurses and patients, or between launch and pre-launch periods. The results do show that sleep in the ICU is neither perfect nor impossible. Most of the sleep scores range between the 30 and 40 percent which indicate a mediocre sleep. Through during the patients scored the noise levels during the launch stage an average of 12.3 where as during the pre-launch stage it was scored at a 31.4. This is 19.1 difference. Conclusion This project took on a very large task in evaluating eleven different alarms at one time. Quality of sleep and noise perception depend on multifaceted variables, especially for patients in the ICU. Some of these other variables that were not taken into consideration were medications, pain levels, and even the patient\u27s hearing ability. Pain levels and some medications can disturb sleep and make the patient more intolerant to noise. Also with the population having an average age of 67 years old in the ICU at Muhlenberg, many of the patients were hard of hearing. This can either affect the results negatively or positively. Many of the patients may not notice some of the noises due to the hearing loss. On the other hand, nurses in return have to speak louder for the patient to understand them while patient teaching or implantation of nursing duties. In future research on sleep and alarm counts in ICU, studies should focus on a younger population and only a few alarms at a time to see if there is any change in data. Before the implementation of the H.U.S.H project, several goals were designated to help better patient outcomes. These goals involved helping alleviate alarm fatigue with a decrease of at least 50% of controllable false alarms, safer decibel levels of 45 dβ during the day and 35 dβ at night, increase patient sleep quality, and improved HCAPHS. From the results, one can see that the false alarms were not reduced by 50% and decibels were decreased after the removal of the 90 decibel ventilator alarms, but after that decibel readings continued to stay around 50.3 decibels. The target score for HCAPH was not met at its 54.15 score and the ICU scored in the first percentile in quietness. These results from the H.U.S.H. project had demonstrated that noise is a huge issue in the hospitals, especially the ICUs. Not only does the noise levels not meet the EPA guideline for background noise levels, it can have implications for patients if they are unable to receive a goodnight\u27s sleep when they are trying to recover from an injury or illness. The health of patients admitted to the hospital should be a priority of any health professional. The correction of high noise levels can possibly lead to better sleep at night. This trickle-down effect can lead to a shorter stay in hospitals, and eventually decreasing the hospital cost. Florence Nightingale once referred to noise as that which damages the patient (Mazer, 2012, p. 350). Future research should take a look to the past to improve the future of patient care. References Dennis, C. M., Lee, R., Knowles Woodard, E., Szalaj, J. J., & Walker, C. A. (2010, August). Benefits of Quiet Time for Neuro-Intensive Care Patients. American Association of NeuroScience Nurses, 42(4), 217-224. Edwards, G. B., & Schuring, L. M. (1993). Pilot Study: Validating Staff Nurses\u27 Observations of Sleep and Wake States Among Critically Ill Patients, Using Polysomnography. American Journal of Critical Care, 2(2), 125-131. Kahn, D. M., Cook, T. E., Carlisle, C. C., Nelson, D. L., Kramer, N. R., & Millman, R. P. (1998, August). Identification and Modification of Environmental Noise in an ICU Setting . Clinical Investigations in Critical Care, 114(2), 535-540. Mazer, S. E. (September/October 2012). Creating a Culture of Safely: Reducing hospital noise. Biomedical Instrumentation & Technology, 350-355. Mazer, S. E. (March/April 2006). Increase Patient Safety by Creating a Quieter Hospital Environment. Biomedical Instrumentation & Technology, 145-146. Olson, T. (2012). Delirium in the Intensive Care Unit Role of the Critical Care Nurse in Early Detection and Treatment. Canadian Association of Critical Care Nurses, 23(4), 32-36. Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Table 1 Target YTD YTD %ile Quietness 54.15 34.3 1 Figure 1

She's single, so what? How are singles perceived compared with people who are married?

'In den vergangenen Jahrzehnten haben sich klassische Beziehungsmuster geändert. Neben der traditionellen Ehe finden sich heute nichteheliche Lebensgemeinschaften, Paare, die getrennte Haushalte führen (LATs) und gleichgeschlechtliche Paare. Daneben wurden Singles zu einer viel diskutierten Gruppe. In Anbetracht der Vielzahl an Lebensformen könnte man annehmen, dass negativ geprägte Stereotype gegenüber Singles zurückgegangen sind. Die Studie zeigt allerdings, dass noch immer verheiratete Personen positiver beurteilt werden als Singles. Beispielsweise werden Singles als einsamer, weniger einfühlsam und weniger fürsorglich eingeschätzt. Es zeigt sich aber auch eine tendenzielle Aufweichung des negativen Stereotyps: (jungen) Singles werden einige positive Eigenschaften zugeschrieben. Hierbei moderieren Merkmale der bewertenden Person die Einschätzung. Vor allem jüngere Frauen und ältere Singles haben ein relativ positives Bild von Singles und beurteilen sie im Vergleich zu verheirateten Personen als geselliger und weltgewandter.' (Autorenreferat)'Over the past few decades, relationship patterns have become more diverse. Besides classical marriage we find cohabitation, romantic partners living apart, and same-sex couples. Furthermore, single people have become an important and intensely discussed segment of society. Due to the increasing plurality of living arrangements, one might assume that stereotypes about singles have changed over the years. The study shows that married people are generally still seen more positively than singles. Singles were seen as more lonely, less warm and caring than married people. However, some positive features are ascribed to singles, too. Importantly, characteristics of the perceiver moderate his or her perceptions. Some groups rated single people as more sophisticated and sociable than married people.' (author's abstract

The effects of self-awareness on body movement indicators of the intention to deceive

A study was conducted to investigate the body movements of participants waiting to be interviewed in one of two conditions: preparing to answer questions truthfully or preparing to lie. The effects of increased self-awareness were also investigated, with half of the participants facing a mirror; the other half facing a blank wall. Analysis of covertly obtained video footage showed a significant interaction for the duration of hand/arm movements between deception level and self-awareness. Without a mirror, participants expecting to lie spent less time moving their hands than those expecting to tell the truth; the opposite was seen in the presence of a mirror. Participants expecting to lie also had higher levels of anxiety and thought that they were left waiting for less time than those expecting to tell the truth. These findings led to the identification of further research areas with the potential to support deception detection in security applications