title: Non-binary youth: Access to gender-affirming primary health care

authors: Clark, Drew B. A.

subjects: Social Sciences

abstract: Background: Transgender (trans) youth who identify outside the gender binary are a growing subpopulation. In this article, we document differences in access to gender-affirming health care between binary and non-binary identified trans youth and explore ways of meeting the health needs of non-binary youth within primary care settings.

Methods: The Canadian Trans Youth Health Survey is a national online survey of trans youth, 14–25 years, conducted in 2013–2014. Among the 839 participants who responded to gender identity items in the survey, 41% identified as non-binary. We compared demographic, health outcome, and health care access responses between non-binary and binary (trans girls/women and trans boys/men) youth.

Results: Non-binary and binary youth were similar in most demographics, including age, geographic distribution, and ethnocultural backgrounds, however a larger proportion (82%) of non-binary youth were assigned female at birth. Older non-binary youth (aged 19–25) were significantly more likely to forego needed healthcare than older binary youth; no significant differences were found between younger (14–18) non-binary and binary youth in foregoing healthcare. Overall, non-binary youth (13%) were significantly less likely than binary youth (52%) to access hormone therapy, but they were more likely than binary youth to report experiencing barriers to accessing hormone therapy when needed.

Conclusions: Non-binary trans youth in Canada report challenges in accessing needed gender-affirming healthcare. Primary care providers are well-situated to integrate a broad range of gender-affirming care services into practice in order to address the unique needs of non-binary youth. Future research is warranted to explore experiences of non-binary youth related to barriers to care and to explore how services can be designed and delivered to better meet the needs of non-binary youth seeking gender-affirming primary care.

text: Non-binary youth: Access to gender-affirming primary health care Background: Transgender (trans) youth who identify outside the gender binary are a growing subpopulation. In this article, we document differences in access to gender-affirming health care between binary and non-binary identified trans youth and explore ways of meeting the health needs of non-binary youth within primary care settings.

Methods: The Canadian Trans Youth Health Survey is a national online survey of trans youth, 14–25 years, conducted in 2013–2014. Among the 839 participants who responded to gender identity items in the survey, 41% identified as non-binary. We compared demographic, health outcome, and health care access responses between non-binary and binary (trans girls/women and trans boys/men) youth.

Results: Non-binary and binary youth were similar in most demographics, including age, geographic distribution, and ethnocultural backgrounds, however a larger proportion (82%) of non-binary youth were assigned female at birth. Older non-binary youth (aged 19–25) were significantly more likely to forego needed healthcare than older binary youth; no significant differences were found between younger (14–18) non-binary and binary youth in foregoing healthcare. Overall, non-binary youth (13%) were significantly less likely than binary youth (52%) to access hormone therapy, but they were more likely than binary youth to report experiencing barriers to accessing hormone therapy when needed.

Conclusions: Non-binary trans youth in Canada report challenges in accessing needed gender-affirming healthcare. Primary care providers are well-situated to integrate a broad range of gender-affirming care services into practice in order to address the unique needs of non-binary youth. Future research is warranted to explore experiences of non-binary youth related to barriers to care and to explore how services can be designed and delivered to better meet the needs of non-binary youth seeking gender-affirming primary care.

year: 2018

title: Predicting glucose level with an adapted branch predictor

authors: Koutny, Tomas

subjects: computer science

abstract: Background and objective
Diabetes mellitus manifests as prolonged elevated blood glucose levels resulting from impaired insulin production. Such high glucose levels over a long period of time damage multiple internal organs. To mitigate this condition, researchers and engineers have developed the closed loop artificial pancreas consisting of a continuous glucose monitor and an insulin pump connected via a microcontroller or smartphone. A problem, however, is how to accurately predict short term future glucose levels in order to exert efficient glucose-level control. Much work in the literature focuses on least prediction error as a key metric and therefore pursues complex prediction methods such a deep learning. Such an approach neglects other important and significant design issues such as method complexity (impacting interpretability and safety), hardware requirements for low-power devices such as the insulin pump, the required amount of input data for training (potentially rendering the method infeasible for new patients), and the fact that very small improvements in accuracy may not have significant clinical benefit.

Methods
We propose a novel low-complexity, explainable blood glucose prediction method derived from the Intel P6 branch predictor algorithm. We use Meta-Differential Evolution to determine predictor parameters on training data splits of the benchmark datasets we use. A comparison is made between our new algorithm and a state-of-the-art deep-learning method for blood glucose level prediction.

Results
To evaluate the new method, the Blood Glucose Level Prediction Challenge benchmark dataset is utilised. On the official test data split after training, the state-of-the-art deep learning method predicted glucose levels 30 min ahead of current time with 96.3% of predicted glucose levels having relative error less than 30% (which is equivalent to the safe zone of the Surveillance Error Grid). Our simpler, interpretable approach prolonged the prediction horizon by another 5 min with 95.8% of predicted glucose levels of all patients having relative error less than 30%.

Conclusions
When considering predictive performance as assessed using the Blood Glucose Level Prediction Challenge benchmark dataset and Surveillance Error Grid metrics, we found that the new algorithm delivered comparable predictive accuracy performance, while operating only on the glucose-level signal with considerably less computational complexity.

text: Predicting glucose level with an adapted branch predictor Background and objective
Diabetes mellitus manifests as prolonged elevated blood glucose levels resulting from impaired insulin production. Such high glucose levels over a long period of time damage multiple internal organs. To mitigate this condition, researchers and engineers have developed the closed loop artificial pancreas consisting of a continuous glucose monitor and an insulin pump connected via a microcontroller or smartphone. A problem, however, is how to accurately predict short term future glucose levels in order to exert efficient glucose-level control. Much work in the literature focuses on least prediction error as a key metric and therefore pursues complex prediction methods such a deep learning. Such an approach neglects other important and significant design issues such as method complexity (impacting interpretability and safety), hardware requirements for low-power devices such as the insulin pump, the required amount of input data for training (potentially rendering the method infeasible for new patients), and the fact that very small improvements in accuracy may not have significant clinical benefit.

Methods
We propose a novel low-complexity, explainable blood glucose prediction method derived from the Intel P6 branch predictor algorithm. We use Meta-Differential Evolution to determine predictor parameters on training data splits of the benchmark datasets we use. A comparison is made between our new algorithm and a state-of-the-art deep-learning method for blood glucose level prediction.

Results
To evaluate the new method, the Blood Glucose Level Prediction Challenge benchmark dataset is utilised. On the official test data split after training, the state-of-the-art deep learning method predicted glucose levels 30 min ahead of current time with 96.3% of predicted glucose levels having relative error less than 30% (which is equivalent to the safe zone of the Surveillance Error Grid). Our simpler, interpretable approach prolonged the prediction horizon by another 5 min with 95.8% of predicted glucose levels of all patients having relative error less than 30%.

Conclusions
When considering predictive performance as assessed using the Blood Glucose Level Prediction Challenge benchmark dataset and Surveillance Error Grid metrics, we found that the new algorithm delivered comparable predictive accuracy performance, while operating only on the glucose-level signal with considerably less computational complexity.

year: 2022

title: Investigating the understanding and implementation of Ka Hikitia, in a New Zealand primary school

authors: Evans, Mary

subjects: Ka Hikitia

abstract: In New Zealand, the education system has underserved Māori learners for many years, with various factors contributing to this underperformance.  Many of these factors stem from racism and bias, conscious or unconscious, inherent in our system (Ministry of Education, 2019b).  Ka Hikitia Accelerating Success 2013 - 2017 (Ministry of Education, 2013a) set about to make transformative change in schools.  This research presents an investigation into the understanding and implementation of Ka Hikitia, in a New Zealand primary school.  
The research was guided by culturally responsive methodologies, bringing together both kaupapa Māori and critical theories (Berryman, 2013b).  Participants’ understandings and how these understandings transferred into their practice was determined through a case study.  The rich information generated within the case was analysed using grounded theory.  The main source of information came from interviewing the management team and teaching staff members.  As an insider/outsider researcher and also a participant, some of my information came from my own observations and knowledge of the school. Some attendance and achievement data held by the school also informed the case.  
The Office of the Auditor-General (2016b) contended that Ka Hikitia was a well-worded policy but, due to a lack of understanding of the policy in many schools, it was poorly implemented nationwide.  In this school, despite the good leadership and hard-working teachers, staff had difficulty fully understanding and therefore implementing the Ka Hikitia strategy as it was intended.  Strong relationships between teachers and students were evident throughout the study, however, stronger relationships were needed between the school and the wider Māori community.  The research shows that, despite the good intentions of the teachers, to fully implement the intent of Ka Hikitia, educators need to be committed to te ao Māori.  
The Ministry of Education plans to relaunch a further iteration of Ka Hikitia this year, supported by another initiative, Te Hurihanganui.  For this iteration to be successful, more effective implementation systems are required that will enable Māori students to enjoy and achieve educational success as Māori.

text: Investigating the understanding and implementation of Ka Hikitia, in a New Zealand primary school In New Zealand, the education system has underserved Māori learners for many years, with various factors contributing to this underperformance.  Many of these factors stem from racism and bias, conscious or unconscious, inherent in our system (Ministry of Education, 2019b).  Ka Hikitia Accelerating Success 2013 - 2017 (Ministry of Education, 2013a) set about to make transformative change in schools.  This research presents an investigation into the understanding and implementation of Ka Hikitia, in a New Zealand primary school.  
The research was guided by culturally responsive methodologies, bringing together both kaupapa Māori and critical theories (Berryman, 2013b).  Participants’ understandings and how these understandings transferred into their practice was determined through a case study.  The rich information generated within the case was analysed using grounded theory.  The main source of information came from interviewing the management team and teaching staff members.  As an insider/outsider researcher and also a participant, some of my information came from my own observations and knowledge of the school. Some attendance and achievement data held by the school also informed the case.  
The Office of the Auditor-General (2016b) contended that Ka Hikitia was a well-worded policy but, due to a lack of understanding of the policy in many schools, it was poorly implemented nationwide.  In this school, despite the good leadership and hard-working teachers, staff had difficulty fully understanding and therefore implementing the Ka Hikitia strategy as it was intended.  Strong relationships between teachers and students were evident throughout the study, however, stronger relationships were needed between the school and the wider Māori community.  The research shows that, despite the good intentions of the teachers, to fully implement the intent of Ka Hikitia, educators need to be committed to te ao Māori.  
The Ministry of Education plans to relaunch a further iteration of Ka Hikitia this year, supported by another initiative, Te Hurihanganui.  For this iteration to be successful, more effective implementation systems are required that will enable Māori students to enjoy and achieve educational success as Māori.

year: 2020