Effect of clinical laboratory practitioner licensing on wages

Professional licensing directly affects about 29% of U.S. workers and is considered a primary means to establish and maintain health care practitioner competence. Clinical laboratory practitioner licensing was largely ignored in the literature with only 2 studies 30 years apart that provided conflicting conclusions regarding wage effects. This research provided the first study of clinical laboratory practitioner licensing effects on wages after controlling for human capital and individual characteristics wage determinants. This nonexperimental correlational study extended the literature on licensing effects on wages, including women\u27s wages and professions not uniformly licensed across 50 states. The theoretical foundation relied on the human capital wage model that wages vary according to human capital investment, namely education and experience. Census 2000 5% Public Use Microdata Sample provided wages and control variable data, including educational attainment, experience, gender, marital status, and children. Using hierarchical regression analysis, this study found clinical laboratory practitioner wages were significantly higher (5.8%) in licensing states compared to nonlicensing states after controlling for these human capital and individual characteristics, R 2change (p \u3c .001). Female clinical laboratory practitioners working in licensing states earned significantly higher wages (5.0%) compared to those in nonlicensing states, R 2change (p \u3c .01). This study has potential for positive social change in clinical laboratory practitioner licensing policy development, implementation, and analysis by providing urgently needed empirical wage data for legislators to make informed decisions on costs to adopting such legislation

Data from: Species discovery and validation in a cryptic radiation of endangered primates: coalescent-based species delimitation in Madagascar's mouse lemurs

Implementation of the coalescent model in a Bayesian framework is an emerging strength in genetically based species delimitation studies. By providing an objective measure of species diagnosis, these methods represent a quantitative enhancement to the analysis of multilocus data, and complement more traditional methods based on phenotypic and ecological characteristics. Recognized as two species 20 years ago, mouse lemurs (genus Microcebus) now comprise more than 20 species, largely diagnosed from mtDNA sequence data. With each new species description, enthusiasm has been tempered with scientific scepticism. Here, we present a statistically justified and unbiased Bayesian approach towards mouse lemur species delimitation. We perform validation tests using multilocus sequence data and two methodologies: (i) reverse-jump Markov chain Monte Carlo sampling to assess the likelihood of different models defined a priori by a guide tree, and (ii) a Bayes factor delimitation test that compares different species-tree models without a guide tree. We assess the sensitivity of these methods using randomized individual assignments, which has been used in bpp studies, but not with Bayes factor delimitation tests. Our results validate previously diagnosed taxa, as well as new species hypotheses, resulting in support for three new mouse lemur species. As the challenge of multiple researchers using differing criteria to describe diversity is not unique to Microcebus, the methods used here have significant potential for clarifying diversity in other taxonomic groups. We echo previous studies in advocating that multiple lines of evidence, including use of the coalescent model, should be trusted to delimit new species

Training to Improve Precision and Accuracy in the Measurement of Fiber Morphology

<div><p>An estimated $7.1 billion dollars a year is spent due to irreproducibility in pre-clinical data from errors in data analysis and reporting. Therefore, developing tools to improve measurement comparability is paramount. Recently, an open source tool, DiameterJ, has been deployed for the automated analysis of scanning electron micrographs of fibrous scaffolds designed for tissue engineering applications. DiameterJ performs hundreds to thousands of scaffold fiber diameter measurements from a single micrograph within a few seconds, along with a variety of other scaffold morphological features, which enables a more rigorous and thorough assessment of scaffold properties. Herein, an online, publicly available training module is introduced for educating DiameterJ users on how to effectively analyze scanning electron micrographs of fibers and the large volume of data that a DiameterJ analysis yields. The end goal of this training was to improve user data analysis and reporting to enhance reproducibility of analysis of nanofiber scaffolds. User performance was assessed before and after training to evaluate the effectiveness of the training modules. Users were asked to use DiameterJ to analyze reference micrographs of fibers that had known diameters. The results showed that training improved the accuracy and precision of measurements of fiber diameter in scanning electron micrographs. Training also improved the precision of measurements of pore area, porosity, intersection density, and characteristic fiber length between fiber intersections. These results demonstrate that the DiameterJ training module improves precision and accuracy in fiber morphology measurements, which will lead to enhanced data comparability.</p></div

General overview of training process for each participant.

<p>General overview of training process for each participant.</p

Plot of participant reported intersection density (A) and reported characteristic length between intersections (B) before and after training.

<p>Gray symbols are individual responses and diamonds are median with first and third quartiles (n = 11). Stars indicate that variances were statistically different between the Pre-Test and Post-Test (Flinger-Killeen's Test, P < 0.05). Dollar sign indicates means were statistically different between the Pre-Test and Post-Test (Wilcoxon Signed Rank Test, P<0.05).</p

Fiber diameter measurements from 10 participants before and after training.

<p>(A,B) Reported fiber diameter and reported standard deviation from each participant in the Pre-Test (A) and Post-Test (B). Note that the test images contained a mix of steel wires with two diameters (48 gauge = 31.1 μm dia.; 50 gauge = 25.6 μm dia.). The dotted lines indicate the known wire diameters. (C) Plot showing prevalence of identifying two diameters of wire (Bimodal) in the steel sample as a function of the Pre-Test and Post-Test. Plot of the number of participants that correctly determined that the test images contained a mix of two wire diameters (bimodal). There was a significant difference between the Pre-Test and Post-Test (Fisher’s Exact Test, P < 0.001). (D) Gray symbols are the individual participant responses and black diamonds are median with first and third quartiles (9 ≤ n ≤ 28). Participant responses that were < 28.35 μm or > 28.35 μm were binned with the 50 gauge (25.6 μm dia., Triangles) or 48 gauge (31.1 μm, Circles) wire, respectively. Note that 28.35 μm is the midpoint between 25.6 μm and 31.1 μm. The star indicates a significant difference between the Pre-Test and Post-Test (variances for 25.6 μm and 3.1. μm were pooled; Levene's Test, 9 ≤ n ≤ 28, P < 0.05). (E) Gray symbols are the errors of the individual participant responses and black diamonds are the error median with first and third quartiles (9 ≤ n ≤ 28). The star indicates a significant difference (2-way analysis of variance, P < 0.05).</p

Plot of participant reported pore areas (A) and reported porosity (B) before and after training.

<p>Grey symbols are the individual responses while diamonds are mean with standard deviation (10 ≤ n ≤ 11). The star indicates that variance in pore area responses for the Pre-Test vs. Post-Test was statistically different (Levene's Test, P < 0.05). Dollar sign indicates means were statistically different between the Pre-Test and Post-Test (Wilcoxon Signed Rank Test, P<0.05).</p

Examples of DiameterJ output.

<p>(A) One of the scanning electron micrographs (SEM) that was used in participant performance assessment is shown. Two gauges of steel wire were entangled to make the sample for the image: 48 gauge (31.1 μm dia.) and 50 gauge (25.6 μm dia.). (B) Segmentation of the SEM image in A. (C) Histogram of all diameters that were calculated from the image by DiameterJ. Red oval indicates “noise”. (D) Red lines indicate location of fiber centerlines that had a dia. between 0 and 20 micrometers and that should be considered “noise”. (E) Outlines of pores (pores touching the edges of the image have been removed). (F) Histogram of characteristic lengths between fiber intersections (bin size 10 μm).</p

Additional file 6: of Modeling, validation and verification of three-dimensional cell-scaffold contacts from terabyte-sized images

Algorithms based on statistical models for segmenting all scaffold types. (DOCX 48 kb