Irina Grossman
Biographic Data
| ID | 4193545 |
|---|---|
| NAME | Irina Grossman |
| GIVEN NAMES | Irina |
| FAMILY NAME | Grossman |
| SIGNATURE | GROSSMAN I |
| AFFILIATIONS | The University of Melbourne |
| ORCID | 0000-0002-5761-6194 |
| VERIFIED | Yes |
| TOTAL WORKS | 8 |
| TOTAL CITATIONS | 10 |
| AUTHOR COUNT | 8 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 1 |
Prevalence and correlates of the food insecurity experience among Australian adults in 2020: Results from the household, income and labour dynamics in Australia population survey
Estimating and projecting the population living with dementia at the local area scale in Australia
As Australia’s population ages, the number of people living with dementia will grow considerably. Understanding local changes in the number of people living with dementia will facilitate suitable planning, costing, and service delivery for health and aged care services. To inform this task, this study presents estimates and projections from 2021 to 2036 of Australians living with dementia at the state and territory level, and at the finer regiona…
Nationally representative estimates of food insecurity during Covid‐19: An investigation of the Food Insecurity Experience Scale in Australia
The purpose of this paper was to measure the prevalence of food insecurity (FI) in Australia during the COVID‐19 pandemic using the 8‐item Food Insecurity Experience Scale (FIES). Employing the Household, Income and Labour Dynamics in Australia (HILDA) survey, Rasch models alongside a discrete method were used to investigate the severity of FI and robustness of the 8‐item FIES during 2020–2021. Our results indicate that during 2020–2021, 6.47% of…
A Practitioner-Oriented Evaluation of Mortality Forecasting Methods: The Case of Australia
Practitioners seeking a suitable mortality model for forecasting population by age and sex are presented with many possible choices from the large and growing academic literature on mortality forecasting. Despite this abundance, there is relatively little practical guidance on selecting the most appropriate models for their needs. This study evaluates the accuracy of mortality forecasting methods and provides guidance on model selection. The eval…
Development and evaluation of probabilistic forecasting methods for small area populations
Planning and development decisions in both the government and business sectors often require small area population forecasts. Unfortunately, current methods often produce forecasts that are inaccurate, particularly for remote areas and those with smaller populations. Such inaccuracy necessitates the development and evaluation of methods to forecast and communicate forecast uncertainty, however, little research has been conducted in this domain fo…
Fail, flip, fix, and feed – Rethinking flipped learning: A review of meta-analyses and a subsequent meta-analysis
The current levels of enthusiasm for flipped learning are not commensurate with and far exceed the vast variability of scientific evidence in its favor. We examined 46 meta-analyses only to find remarkably different overall effects, raising the question about possible moderators and confounds, showing the need to control for the nature of the intervention. We then conducted a meta-analysis of 173 studies specifically coding the nature of the flip…
Can machine learning improve small area population forecasts? A forecast combination approach
Methods for Small Area Population Forecasts: State-of-the-Art and Research Needs
Methods for Small Area Population Forecasts: State-of-the-Art and Research Needs
Nationally representative estimates of food insecurity during Covid‐19: An investigation of the Food Insecurity Experience Scale in Australia
The purpose of this paper was to measure the prevalence of food insecurity (FI) in Australia during the COVID‐19 pandemic using the 8‐item Food Insecurity Experience Scale (FIES). Employing the Household, Income and Labour Dynamics in Australia (HILDA) survey, Rasch models alongside a discrete method were used to investigate the severity of FI and robustness of the 8‐item FIES during 2020–2021. Our results indicate that during 2020–2021, 6.47% of…
Fail, flip, fix, and feed – Rethinking flipped learning: A review of meta-analyses and a subsequent meta-analysis
The current levels of enthusiasm for flipped learning are not commensurate with and far exceed the vast variability of scientific evidence in its favor. We examined 46 meta-analyses only to find remarkably different overall effects, raising the question about possible moderators and confounds, showing the need to control for the nature of the intervention. We then conducted a meta-analysis of 173 studies specifically coding the nature of the flip…
Can machine learning improve small area population forecasts? A forecast combination approach
Methods for Small Area Population Forecasts: State-of-the-Art and Research Needs
Development and evaluation of probabilistic forecasting methods for small area populations
Planning and development decisions in both the government and business sectors often require small area population forecasts. Unfortunately, current methods often produce forecasts that are inaccurate, particularly for remote areas and those with smaller populations. Such inaccuracy necessitates the development and evaluation of methods to forecast and communicate forecast uncertainty, however, little research has been conducted in this domain fo…
Estimating and projecting the population living with dementia at the local area scale in Australia
As Australia’s population ages, the number of people living with dementia will grow considerably. Understanding local changes in the number of people living with dementia will facilitate suitable planning, costing, and service delivery for health and aged care services. To inform this task, this study presents estimates and projections from 2021 to 2036 of Australians living with dementia at the state and territory level, and at the finer regiona…
Nationally representative estimates of food insecurity during Covid‐19: An investigation of the Food Insecurity Experience Scale in Australia
The purpose of this paper was to measure the prevalence of food insecurity (FI) in Australia during the COVID‐19 pandemic using the 8‐item Food Insecurity Experience Scale (FIES). Employing the Household, Income and Labour Dynamics in Australia (HILDA) survey, Rasch models alongside a discrete method were used to investigate the severity of FI and robustness of the 8‐item FIES during 2020–2021. Our results indicate that during 2020–2021, 6.47% of…
A Practitioner-Oriented Evaluation of Mortality Forecasting Methods: The Case of Australia
Practitioners seeking a suitable mortality model for forecasting population by age and sex are presented with many possible choices from the large and growing academic literature on mortality forecasting. Despite this abundance, there is relatively little practical guidance on selecting the most appropriate models for their needs. This study evaluates the accuracy of mortality forecasting methods and provides guidance on model selection. The eval…
Prevalence and correlates of the food insecurity experience among Australian adults in 2020: Results from the household, income and labour dynamics in Australia population survey
Computer Science (5 works) · Population (5 works) · Artificial Intelligence (4 works) · demographic modeling and climate adaptation (3 works) · Econometrics (3 works) · Geography (3 works) · Insurance, Mortality, Demography, Risk Management (3 works) · Medicine (3 works) · Agriculture (2 works) · Consensus forecast (2 works)