Utilizing Academic Advising Services to Improve First-Year Student Retention Outcomes

Student retention is an issue that impacts most post-secondary institutions in Canada. For the majority of these institutions, the student retention issue is most acute in first-year students. This Organizational Improvement Plan (OIP) seeks to address the Problem of Practice (PoP) of poor first-year student retention outcomes within a mid-sized post-secondary institution in Ontario (University X). Specifically, it explores the vital role that academic advisors play in retaining post-secondary students. With flat domestic enrollment numbers and challenges facing the international student market coupled with a restructured provincial government funding model, retaining students has become more important than ever before. Failure to adequately pivot to address the institution\u27s challenges may result in significant, undesirable financial consequences for the institution. Unfortunately, in its current formation, the academic advisors are not well-positioned to optimally impact student retention outcomes. Central to this OIP are the recommended leadership approaches and the adequate framing of the Problem of Practice. Transactional and transformational leadership approaches will be implemented, and the problem is framed using Bolman & Deal’s four frames along with a PEST analysis. Resistance plays a central role within this OIP and establishing ways to address it are explored through utilizing Kotter’s Eight-Stage Change Process and Duck’s Five-Stage Change Curve as a roadmap for the change. In addition, using a PDSA cycle to monitor and evaluate the success of the desired changes coupled with the implementation of a strong communication plan of the changes is central to the success of the OIP

Reducing Development and Operations Costs using NASA's "GMSEC" Systems Architecture

This viewgraph presentation reviews the role of Goddard Mission Services Evolution Center (GMSEC) in reducing development and operation costs in handling the massive data from NASA missions. The goals of GMSEC systems architecture development are to (1) Simplify integration and development, (2)Facilitate technology infusion over time, (3) Support evolving operational concepts, and (4) All for mix of heritage, COTS and new components. First 3 missions (i.e., Tropical Rainforest Measuring Mission (TRMM), Small Explorer (SMEX) missions - SWAS, TRACE, SAMPEX, and ST5 3-Satellite Constellation System) each selected a different telemetry and command system. These results show that GMSEC's message-bus component-based framework architecture is well proven and provides significant benefits over traditional flight and ground data system designs. The missions benefit through increased set of product options, enhanced automation, lower cost and new mission-enabling operations concept options

Compositional Program Generation for Systematic Generalization

Compositional generalization is a key ability of humans that enables us to learn new concepts from only a handful examples. Machine learning models, including the now ubiquitous transformers, struggle to generalize in this way, and typically require thousands of examples of a concept during training in order to generalize meaningfully. This difference in ability between humans and artificial neural architectures, motivates this study on a neuro-symbolic architecture called the Compositional Program Generator (CPG). CPG has three key features: modularity, type abstraction, and recursive composition, that enable it to generalize both systematically to new concepts in a few-shot manner, as well as productively by length on various sequence-to-sequence language tasks. For each input, CPG uses a grammar of the input domain and a parser to generate a type hierarchy in which each grammar rule is assigned its own unique semantic module, a probabilistic copy or substitution program. Instances with the same hierarchy are processed with the same composed program, while those with different hierarchies may be processed with different programs. CPG learns parameters for the semantic modules and is able to learn the semantics for new types incrementally. Given a context-free grammar of the input language and a dictionary mapping each word in the source language to its interpretation in the output language, CPG can achieve perfect generalization on the SCAN and COGS benchmarks, in both standard and extreme few-shot settings.Comment: 7 pages of text with 1 page of reference

Associations between incident breast cancer and ambient concentrations of nitrogen dioxide from a national land use regression model in the Canadian National Breast Screening Study

Background: Air pollution has been classified as a human carcinogen based largely on epidemiological studies of lung cancer. Recent research suggests that exposure to ambient air pollution increases the risk of female breast cancer especially in premenopausal women. Methods: Our objective was to determine the association between residential exposure to ambient nitrogen dioxide (NO2) and newly diagnosed cases of invasive breast cancer in a cohort of 89,247 women enrolled in the Canadian National Breast Screening Study between 1980 and 1985.

MISMATCH: Fine-grained Evaluation of Machine-generated Text with Mismatch Error Types

With the growing interest in large language models, the need for evaluating the quality of machine text compared to reference (typically human-generated) text has become focal attention. Most recent works focus either on task-specific evaluation metrics or study the properties of machine-generated text captured by the existing metrics. In this work, we propose a new evaluation scheme to model human judgments in 7 NLP tasks, based on the fine-grained mismatches between a pair of texts. Inspired by the recent efforts in several NLP tasks for fine-grained evaluation, we introduce a set of 13 mismatch error types such as spatial/geographic errors, entity errors, etc, to guide the model for better prediction of human judgments. We propose a neural framework for evaluating machine texts that uses these mismatch error types as auxiliary tasks and re-purposes the existing single-number evaluation metrics as additional scalar features, in addition to textual features extracted from the machine and reference texts. Our experiments reveal key insights about the existing metrics via the mismatch errors. We show that the mismatch errors between the sentence pairs on the held-out datasets from 7 NLP tasks align well with the human evaluation.Comment: Accepted at ACL 2023 (ACL Findings Long

Indirect adjustment for multiple missing variables applicable to environmental epidemiology

AbstractObjectivesDevelop statistical methods for survival models to indirectly adjust hazard ratios of environmental exposures for missing risk factors.MethodsA partitioned regression approach for linear models is applied to time to event survival analyses of cohort study data. Information on the correlation between observed and missing risk factors is obtained from ancillary data sources such as national health surveys. The relationship between the missing risk factors and survival is obtained from previously published studies. We first evaluated the methodology using simulations, by considering the Weibull survival distribution for a proportional hazards regression model with varied baseline functions, correlations between an adjusted variable and an adjustment variable as well as selected censoring rates. Then we illustrate the method in a large, representative Canadian cohort of the association between concentrations of ambient fine particulate matter and mortality from ischemic heart disease.ResultsIndirect adjustment for cigarette smoking habits and obesity increased the fine particulate matter-ischemic heart disease association by 3%–123%, depending on the number of variables considered in the adjustment model due to the negative correlation between these two risk factors and ambient air pollution concentrations in Canada. The simulations suggested that the method yielded small relative bias (<40%) for most cohort designs encountered in environmental epidemiology.ConclusionsThis method can accommodate adjustment for multiple missing risk factors simultaneously while accounting for the associations between observed and missing risk factors and between missing risk factors and health endpoints

Associations between ambient air pollution and daily mortality in a cohort of congestive heart failure: Case-crossover and nested case-control analyses using a distributed lag nonlinear model.

BACKGROUND: Persons with congestive heart failure may be at higher risk of the acute effects related to daily fluctuations in ambient air pollution. To meet some of the limitations of previous studies using grouped-analysis, we developed a cohort study of persons with congestive heart failure to estimate whether daily non-accidental mortality were associated with spatially-resolved, daily exposures to ambient nitrogen dioxide (NO2) and ozone (O3), and whether these associations were modified according to a series of indicators potentially reflecting complications or worsening of health. METHODS: We constructed the cohort from the linkage of administrative health databases. Daily exposure was assigned from different methods we developed previously to predict spatially-resolved, time-dependent concentrations of ambient NO2 (all year) and O3 (warm season) at participants' residences. We performed two distinct types of analyses: a case-crossover that contrasts the same person at different times, and a nested case-control that contrasts different persons at similar times. We modelled the effects of air pollution and weather (case-crossover only) on mortality using distributed lag nonlinear models over lags 0 to 3 days. We developed from administrative health data a series of indicators that may reflect the underlying construct of "declining health", and used interactions between these indicators and the cross-basis function for air pollutant to assess potential effect modification. RESULTS: The magnitude of the cumulative as well as the lag-specific estimates of association differed in many instances according to the metric of exposure. Using the back-extrapolation method, which is our preferred exposure model, we found for the case-crossover design a cumulative mean percentage changes (MPC) in daily mortality per interquartile increment in NO2 (8.8 ppb) of 3.0% (95% CI: -0.4, 6.6%) and for O3 (16.5 ppb) 3.5% (95% CI: -4.5, 12.1). For O3 there was strong confounding by weather (unadjusted MPC = 7.1%; 95% CI: 1.7, 12.7%). For the nested case-control approach the cumulative MPC for NO2 in daily mortality was 2.9% (95% CI: -0.9, 6.9%) and for O3 7.3% (95% CI: 3.0, 11.9%). We found evidence of effect modification between daily mortality and cumulative NO2 and O3 according to the prescribed dose of furosemide in the nested case-control analysis, but not in the case-crossover analysis. CONCLUSIONS: Mortality in congestive heart failure was associated with exposure to daily ambient NO2 and O3 predicted from a back-extrapolation method using a land use regression model from dense sampling surveys. The methods used to assess exposure can have considerable influence on the estimated acute health effects of the two air pollutants

Real-time infrared spectroscopy coupled with blind source separation for nuclear waste process monitoring

On-line infrared absorbance spectroscopy enables rapid measurement of solution-phase molecular species. Many spectra-to-concentration models exist for spectral data, with some models able to handle overlapping spectral bands and nonlinearities. However, model accuracy is limited by the quality of training data used in model fitting. The process spectra of nuclear waste simulants at the Savannah River Site display incongruity between training and process spectra; the glycolate spectral signature in the training data does not match the glycolate signature in Savannah River National Laboratory process data. A novel blind source separation algorithm is proposed that preprocesses spectral data so that process spectra more closely resemble training spectra, thereby improving model quantification accuracy when unexpected sources of variation appear in process spectra. The novel blind source separation preprocessing algorithm is shown to improve nitrate quantification from an R2 of 0.934 to 0.988 and from 0.267 to 0.978 in two instances analyzing nuclear waste simulants from the Slurry Receipt Adjustment Tank and Slurry Mix Evaporator cycle at the Savannah River Site

Complex relationships between greenness, air pollution, and mortality in a population-based Canadian cohort

Background: Epidemiological studies have consistently demonstrated that exposure to fine particulate matter (PM 2.5 )is associated with increased risks of mortality. To a lesser extent, a series of studies suggest that living in greener areas is associated with reduced risks of mortality. Only a handful of studies have examined the interplay between PM 2.5 , greenness, and mortality. Methods: We investigated the role of residential greenness in modifying associations between long-term exposures to PM 2.5 and non-accidental and cardiovascular mortality in a national cohort of non-immigrant Canadian adults (i.e., the 2001 Canadian Census Health and Environment Cohort). Specifically, we examined associations between satellite-derived estimates of PM 2.5 exposure and mortality across quintiles of greenness measured within 500 m of individual's place of residence during 11 years of follow-up. We adjusted our survival models for many personal and contextual measures of socioeconomic position, and residential mobility data allowed us to characterize annual changes in exposures. Results: Our cohort included approximately 2.4 million individuals at baseline, 194,270 of whom died from non-accidental causes during follow-up. Adjustment for greenness attenuated the association between PM 2.5 and mortality (e.g., hazard ratios (HRs)and 95% confidence intervals (CIs)per interquartile range increase in PM 2.5 in models for non-accidental mortality decreased from 1.065 (95% CI: 1.056–1.075)to 1.041 (95% CI: 1.031–1.050)). The strength of observed associations between PM 2.5 and mortality decreased as greenness increased. This pattern persisted in models restricted to urban residents, in models that considered the combined oxidant capacity of ozone and nitrogen dioxide, and within neighbourhoods characterised by high or low deprivation. We found no increased risk of mortality associated with PM 2.5 among those living in the greenest areas. For example, the HR for cardiovascular mortality among individuals in the least green areas was 1.17 (95% CI: 1.12–1.23)compared to 1.01 (95% CI: 0.97–1.06)among those in the greenest areas. Conclusions: Studies that do not account for greenness may overstate the air pollution impacts on mortality. Residents in deprived neighbourhoods with high greenness benefitted by having more attenuated associations between PM 2.5 and mortality than those living in deprived areas with less greenness. The findings from this study extend our understanding of how living in greener areas may lead to improved health outcomes