title: Mixed pixel return separation for a full-field ranger

authors: Godbaz, John Peter

subjects: lidar

abstract: Full-field amplitude modulated continuous wave range imagers commonly suffer from the mixed pixel problem. This problem is caused by the integration of light from multiple sources by a single pixel, particularly around the edges of objects, resulting in erroneous range measurements. In this paper we present a method for identifying the intensity and range of multiple return values within each pixel, using the harmonic content of the heterodyne beat waveform. Systems capable of measurements at less than 90 degree phase shifts can apply these methods. Our paper builds on previous simulation based work and uses real range data. The method involves the application of the Levy-Fullagar algorithm and the use of the cyclic nature of the beat waveform to extract the mean noise power. We show that this method enables the separation of multiple range sources and also decreases overall ranging error by 30% in the single return case. Error in the two return case was found to increase substantially as relative intensity of the return decreased.

text: Mixed pixel return separation for a full-field ranger Full-field amplitude modulated continuous wave range imagers commonly suffer from the mixed pixel problem. This problem is caused by the integration of light from multiple sources by a single pixel, particularly around the edges of objects, resulting in erroneous range measurements. In this paper we present a method for identifying the intensity and range of multiple return values within each pixel, using the harmonic content of the heterodyne beat waveform. Systems capable of measurements at less than 90 degree phase shifts can apply these methods. Our paper builds on previous simulation based work and uses real range data. The method involves the application of the Levy-Fullagar algorithm and the use of the cyclic nature of the beat waveform to extract the mean noise power. We show that this method enables the separation of multiple range sources and also decreases overall ranging error by 30% in the single return case. Error in the two return case was found to increase substantially as relative intensity of the return decreased.

year: 2008

title: "I teach them. I have no choice": experiences of primary care among transgender people in Aotearoa New Zealand.

authors: Tan, Kyle K. H.

subjects: Health Services Accessibility

abstract: AIM: This study aims to report primary care experiences among transgender people in Aotearoa New Zealand based on quantitative and qualitative data from a nationwide community-based survey of transgender people. 
METHODS: Subsamples with a usual general practitioner were employed from the 2018 Counting Ourselves Survey (n=871) and the 2018/19 New Zealand Health Survey to assess inequities between these samples in primary care experiences and barriers. Guided by Andersen's Behavioural Model of healthcare access, we conducted a content analysis on comments from Counting Ourselves participants (n=153) to identify themes about issues of concern for transgender people when accessing primary care. RESULTS: Transgender participants had greater risk of feeling no confidence in their GPs (Mdifference=0.22; Cohen's d=0.39), reporting barriers accessing primary care due to cost (38.4% vs 17.4%; RR=2.21), and transport issues (13.5% vs 3.0%; RR=4.58) compared to the general population. Content analysis uncovered how transgender people's primary care experiences are shaped by healthcare environments, predisposing characteristics, and enabling resources. 
CONCLUSION: Our findings indicate ways to ensure primary care services are inclusive so that all transgender people feel welcome. This requires all primary healthcare professionals to demonstrate core trans-specific cultural safety when providing healthcare to transgender patients.

text: "I teach them. I have no choice": experiences of primary care among transgender people in Aotearoa New Zealand. AIM: This study aims to report primary care experiences among transgender people in Aotearoa New Zealand based on quantitative and qualitative data from a nationwide community-based survey of transgender people. 
METHODS: Subsamples with a usual general practitioner were employed from the 2018 Counting Ourselves Survey (n=871) and the 2018/19 New Zealand Health Survey to assess inequities between these samples in primary care experiences and barriers. Guided by Andersen's Behavioural Model of healthcare access, we conducted a content analysis on comments from Counting Ourselves participants (n=153) to identify themes about issues of concern for transgender people when accessing primary care. RESULTS: Transgender participants had greater risk of feeling no confidence in their GPs (Mdifference=0.22; Cohen's d=0.39), reporting barriers accessing primary care due to cost (38.4% vs 17.4%; RR=2.21), and transport issues (13.5% vs 3.0%; RR=4.58) compared to the general population. Content analysis uncovered how transgender people's primary care experiences are shaped by healthcare environments, predisposing characteristics, and enabling resources. 
CONCLUSION: Our findings indicate ways to ensure primary care services are inclusive so that all transgender people feel welcome. This requires all primary healthcare professionals to demonstrate core trans-specific cultural safety when providing healthcare to transgender patients.

year: 2022

title: The Māori health data governance model: A planning and protocol checklist

authors: Kremer, Lisa

abstract: Māori health data (MHD) governance is a critical enabler of achieving health equity for Māori. The Māori Data Governance Model (the Model) provides a framework for all stages of Māori data handling but has yet to be meaningfully implemented within health settings. This study aimed to meet the need for health-specific guidance by adapting the Model to create an MHD governance checklist. This checklist can be used by health researchers and agencies to develop and embed their own MHD governance protocol (e.g. embed within study protocol).

text: The Māori health data governance model: A planning and protocol checklist Māori health data (MHD) governance is a critical enabler of achieving health equity for Māori. The Māori Data Governance Model (the Model) provides a framework for all stages of Māori data handling but has yet to be meaningfully implemented within health settings. This study aimed to meet the need for health-specific guidance by adapting the Model to create an MHD governance checklist. This checklist can be used by health researchers and agencies to develop and embed their own MHD governance protocol (e.g. embed within study protocol).

year: 2025