An Experimental Investigation of Preference Misrepresentation in the Residency Match

The development and deployment of matching procedures that incentivize truthful preference reporting is considered one of the major successes of market design research. In this study, we test the degree to which these procedures succeed in eliminating preference misrepresentation. We administered an online experiment to 1,714 medical students immediately after their participation in the medical residency match--a leading field application of strategy-proof market design. When placed in an analogous, incentivized matching task, we find that 23% of participants misrepresent their preferences. We explore the factors that predict preference misrepresentation, including cognitive ability, strategic positioning, overconfidence, expectations, advice, and trust. We discuss the implications of this behavior for the design of allocation mechanisms and the social welfare in markets that use them

An assessment of failure to rescue derived from routine NHS data as a nursing sensitive patient safety indicator (report to Policy Research Programme)

Objectives: This study aims to assess the potential for deriving 2 mortality based failure to rescue indicators and a proxy measure, based on exceptionally long length of stay, from English hospital administrative data by exploring change in coding practice over time and measuring associations between failure to rescue and factors which would suggest indicators derived from these data are valid.Design: Cross sectional observational study of routinely collected administrative data.Setting: 146 general acute hospital trusts in England.Participants: Discharge data from 66,100,672 surgical admissions (1997 to 2009).Results: Median percentage of surgical admissions with at least one secondary diagnosis recorded increased from 26% in 1997/8 to 40% in 2008/9. The failure to rescue rate for a hospital appears to be relatively stable over time: inter-year correlations between 2007/8 and 2008/9 were r=0.92 to r=0.94. No failure to rescue indicator was significantly correlated with average number of secondary diagnoses coded per hospital. Regression analyses showed that failure to rescue was significantly associated (p<0.05) with several hospital characteristics previously associated with quality including staffing levels. Higher medical staffing (doctors + nurses) per bed and more doctors relative to the number of nurses were associated with lower failure to rescue. Conclusion: Coding practice has improved, and failure to rescue can be derived from English administrative data. The suggestion that it is particularly sensitive to nursing is not clearly supported. Although the patient population is more homogenous than for other mortality measures, risk adjustment is still required

Enabling Fine-Grain Restricted Coset Coding Through Word-Level Compression for PCM

Phase change memory (PCM) has recently emerged as a promising technology to meet the fast growing demand for large capacity memory in computer systems, replacing DRAM that is impeded by physical limitations. Multi-level cell (MLC) PCM offers high density with low per-byte fabrication cost. However, despite many advantages, such as scalability and low leakage, the energy for programming intermediate states is considerably larger than programing single-level cell PCM. In this paper, we study encoding techniques to reduce write energy for MLC PCM when the encoding granularity is lowered below the typical cache line size. We observe that encoding data blocks at small granularity to reduce write energy actually increases the write energy because of the auxiliary encoding bits. We mitigate this adverse effect by 1) designing suitable codeword mappings that use fewer auxiliary bits and 2) proposing a new Word-Level Compression (WLC) which compresses more than 91% of the memory lines and provides enough room to store the auxiliary data using a novel restricted coset encoding applied at small data block granularities. Experimental results show that the proposed encoding at 16-bit data granularity reduces the write energy by 39%, on average, versus the leading encoding approach for write energy reduction. Furthermore, it improves endurance by 20% and is more reliable than the leading approach. Hardware synthesis evaluation shows that the proposed encoding can be implemented on-chip with only a nominal area overhead.Comment: 12 page

Which Facial Features Are Central in Impression Formation?

Which facial characteristics do people rely on when forming personality impressions? Previous research has uncovered an array of facial features that influence people’s impressions. Even though some (classes of) features, such as resemblances to emotional expressions or facial width-to-height ratio (fWHR), play a central role in theories of social perception, their relative importance in impression formation remains unclear. Here, we model faces along a wide range of theoretically important dimensions and use machine learning techniques to test how well 28 features predict impressions of trustworthiness and dominance in a diverse set of 597 faces. In line with overgeneralization theory, emotion resemblances were most predictive of both traits. Other features that have received a lot of attention in the literature, such as fWHR, were relatively uninformative. Our results highlight the importance of modeling faces along a wide range of dimensions to elucidate their relative importance in impression formation