the medical librarians guide to better sex and health for men over 40 pdf

the medical librarians guide to better sex and health for men over 40 pdf

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the medical librarians guide to better sex and health for men over 40 pdfTry these foods Strong pelvic floor muscles can go a long way toward warding off incontinence. They also work for men plagued by incontinence. They don't reap the benefits of the exercises. Start by lying on your back until you get the feel of contracting the pelvic floor muscles. When you have the hang of it, practice while sitting and standing. Don't contract your abdominal, leg, or buttock muscles, or lift your pelvis. Place a hand gently on your belly to detect unwanted abdominal action. Work your way up to 10-second contractions and relaxations Spreading them throughout the day is better than doing them all at once. Since these are stealth exercises that no one notices but you, try to sneak in a few when waiting at a stoplight, riding an elevator, or standing in a grocery line. If you have the urge to urinate and doubt you are going to make it to the toilet, doing Kegels may get you safely to a restroom. No content on this site, regardless of date. While well-intentioned male leaders reported a respectful and inclusive office environment, some female faculty and staff experienced unconscious bias and microaggressions. And while both male and female leaders thought they were making strides in diversifying our fellowship program, alumni found their progress lacking. The team adopted a “ small wins ” model of change that focuses on setting and achieving narrow, attainable goals to produce a sense of success that is contagious and builds momentum for larger gains and systemic transformation. They’ve identified 10 small wins organizations can undertake immediately to help build a staircase that will lead to larger success. While health care is often considered a leader in gender diversity, with women making up more than half its workforce, health technology looks much more like the tech industry when it comes to gender, race, and other forms of diversity.http://hetodon.com/fckeditorfiles/brand-ambassador-training-manual.xml

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Our recent survey of 403 people working in health tech, for example, found that 90 of respondents were in a company where the majority of senior leaders are men. Our latest research about gender in the health tech workplace made the challenge we face much clearer: Many men seem to think sufficient progress has been made and that women now enjoy equal standing and opportunity. Women, on the other hand, still perceive a highly unequal workplace rife with systematic barriers. These experiences of discrimination are all too familiar to many professional women, particularly in STEM fields. For instance, 80 of responding men believe their workplace “empowers women to reach their full potential,” while just 36 of women agree. Only 35 of women believe their organization’s criteria for promotion is the same for both genders, while 84 of men say it is equitable. Half of women involved in fundraising felt men and women on their pitch team were treated differently by investors, compared to one in 10 men. For instance, when asked what holds women back from senior leadership positions, the most common response from men was women’s desire to balance work and family. Women, in contrast, said the biggest barriers to advancement are systemic factors like stereotyping and exclusion from networks of communication and influence. Yet these struggles have real consequences for their success. Women in our survey were far more likely to have witnessed or experienced gender discrimination and to report gender disparities in compensation and opportunities for advancement. They had lower levels of job satisfaction than their male colleagues and were twice as likely to be considering leaving their current job. An internal survey of Biodesign’s faculty, staff, and program alumni revealed some of the same patterns as our industry-wide research.http://www.bmsk.ru/images/static/branch-organization-manual.xml While our well-intentioned male leaders reported a respectful and inclusive office environment, some of our female faculty and staff experienced unconscious bias and microaggressions. In addition, although both male and female leaders thought we were making strides in diversifying our fellowship program, alumni found our progress lacking. This approach focuses on setting and achieving narrow, attainable goals to produce a sense of success that is contagious and builds momentum for larger gains and systemic transformation. It also emphasizes improving organizational processes rather than relying solely on individual-level transformation. Our internal survey revealed that female faculty members routinely helped administrative staff with cleanup tasks at important events, such as resetting meeting spaces during breaks, while their male counterparts walked away. As anticipated by our research, male faculty members were largely unaware of this pattern and its potential implication that certain administrative tasks are “women’s work.” Once the issue was surfaced, however, there was immediate buy-in on new standards of shared responsibility for lending a hand. Creating space for discussion around these issues makes it easier for team members to voice concerns and suggestions. It also demonstrates leaders’ commitment to addressing diversity and makes internal disparities more visible to those who have failed to see them. We broadened our recruiting efforts by targeting diverse university engineering programs and technical societies for women and underrepresented groups. We balanced genders on our faculty and alumni application review committees and included more reviewers of color.http://fscl.ru/content/3m-maintenance-manual-navy We adjusted our application scoring system to reduce the emphasis on education and professional experience, which might disadvantage individuals without a privileged background, and increase the value placed on categories such as leadership, creativity, and essays where applicants tell their personal stories. But we believe small wins add up. Historically only about one-fourth of our fellowship graduates have been women, but 40 of those accepted to the program for 2020-21 are women and two out of 12 are Black. We are tracking our metrics to hold ourselves accountable and continue to raise the bar. It includes ideas like making sure everyone has an opportunity to speak during meetings, asking rather than making assumptions about what responsibilities or challenges an employee might be willing to take on (such as a project or role that involves substantial travel), and committing to recruiting and screening at least three qualified candidates for the next job opening who would diversify the team in a substantial way. Make sure everyone has the opportunity to speak and nobody is regularly interrupted. Identify and acknowledge where ideas originate. Don’t be distracted by who speaks loudest or last. Speak up if you hear colleagues disparage those who utilize flexible work arrangements. This will prevent individuals with child or elder care responsibilities from having to make special arrangements to participate. Consider lunch or late afternoon activities so as not to exclude people who can’t easily attend after hours. Ask them — and then actively support their choices. Mentors (both male and female) play a key role in encouraging and empowering women to advance in their careers. Unsure where to start.Research has shown that when men get involved in diversity initiatives, the company makes greater progress toward gender parity. But a crucial first step is making sure we’re all on the same page.http://cool-grey.com/images/bowers-u0026-wilkins-panorama-2-manual.pdf The vast majority of men in our industry survey think women in their workplaces have as much strategic ability, commitment, and other professional capabilities as men. Yet many of them are blind to the widespread systemic barriers their female colleagues still face. In our experience, the same is often true of white people in health tech, many of whom support racial diversity in theory but are largely unaware of the discrimination people of color face in their companies and how they may contribute to it. People in power in health tech and other industries must open their eyes to the continuing challenges that their colleagues from underrepresented groups face. Organizations need to surface the inequalities that are so obvious to women, particularly women of color, but largely invisible to (mostly white and male) leadership. While exposing these issues to the light may seem like a small win, closing the perception gap is an essential first step on the path toward equality. Harvard Business Publishing is an affiliate of Harvard Business School. There is unprecedented urgency to understand who is most at risk of severe outcomes, and this requires new approaches for the timely analysis of large datasets. Here we used OpenSAFELY to examine factors associated with COVID-19-related death. Primary care records of 17,278,392 adults were pseudonymously linked to 10,926 COVID-19-related deaths. More patient records are rapidly being added to OpenSAFELY, we will update and extend our results regularly. On the same day in the UK, there had been 206,715 confirmed cases of COVID-19, and 30,615 COVID-19-related deaths 3. Age and gender are well-established risk factors for severe COVID-19 outcomes: over 90 of the COVID-19-related deaths in the UK have been in people over 60, and 60 in men 4. Various pre-existing conditions have also been associated with increased risk. The risks associated with smoking are unclear 9, 10, 11.http://www.naturapreserved.com/wp-content/plugins/formcraft/file-upload/server/content/files/16274d9d08afff---brother-innovis-900d-manual.pdf People from Black and minority ethnic groups are at increased risk of poor outcomes from COVID-19, for reasons that are unclear 12, 13. Patient care is typically managed through electronic health records, which are commonly used in research. However traditional approaches to the analysis of electronic health records rely on intermittent extracts of small samples of historic data. Evaluating a rapidly arising novel cause of death requires a new approach. We therefore set out to deliver a secure analytics platform inside the data centre of major electronic health records vendors, running across the full, linked and pseudonymized electronic health records of a very large population of NHS patients, to determine factors that are associated with COVID-19-related death in England. Associations with COVID-19-related death In total, 17,278,392 adults were included (Fig. 1; cohort description in Table 1 ). Eleven per cent of individuals (1,851,868) had ethnicity recorded as mixed, South Asian, Black or other (hereafter referred to as Black and minority ethnic, BAME). There were missing data for body mass index (3,751,769; 22), smoking status (720,923; 4), ethnicity (4,560,113; 26) and blood pressure (1,715,095; 10). COVID-19-related death was recorded in linked death registration data for 10,926 of the study population. Fig. 1: Flow diagram of the cohort. Plots show COVID-19-related death over time by age and sex. Table 2 Hazard ratios and 95 confidence intervals for COVID-19-related death Full size table Fig. 3: Estimated hazard ratios for each patient characteristic from a multivariable Cox model. Hazard ratios are shown on a log scale. Error bars represent the limits of the 95 confidence interval for the hazard ratio. All hazard ratios are adjusted for all other factors listed other than ethnicity. Ethnicity estimates are from a separate model among those individuals for whom complete ethnicity data were available, and are fully adjusted for other covariates.akaruiedu.com/uploaded/ckeditors/files/8990a-manual.pdf Full size image People from all BAME groups were at higher risk than those of white ethnicity. BAME ethnicity has previously been found to be associated with an increased risk of COVID-19 infection and poor outcomes 12, 13, 15. Our findings show that only a small part of the excess risk is explained by a higher prevalence of medical problems such as cardiovascular disease or diabetes among BAME people, or by higher levels of deprivation. We found a consistent pattern of increasing risk with greater deprivation, with the most deprived quintile having a hazard ratio of 1.79 compared to the least deprived, consistent with recent national statistics 16. Again, very little of this increased risk was explained by pre-existing disease or clinical factors, suggesting that other social factors have an important role. For other cancers, hazard ratios were smaller and increased risks were associated mainly with recent diagnoses. Our results showing that severe asthma is associated with a higher risk are notable, as early data suggested that asthma was under-represented in patients with COVID-19 who were hospitalized or had severe outcomes 17, 18. This and other comorbidities could be consequences of smoking, highlighting that the fully adjusted smoking hazard ratio cannot be interpreted causally owing to the inclusion of factors that are likely to mediate smoking effects. Discussion This secure analytics platform operating across NHS patient records of over 17 million adults and 6 million children was used to identify, quantify and analyse factors associated with COVID-19-related death in one of the largest cohort studies on this topic conducted by any country so far. Most comorbidities were associated with increased risk, including cardiovascular disease, diabetes, respiratory disease (including severe asthma), obesity, a history of haematological malignancy or recent other cancer, kidney, liver and neurological diseases, and autoimmune conditions.http://counterreaction.net/wp-content/plugins/formcraft/file-upload/server/content/files/16274d9e0d98a6---brother-innovis-cs-8060-manual.pdf South Asian and Black people had a substantially higher risk of COVID-19-related death than white people, and this was only partly attributable to comorbidities, deprivation or other factors. A strong association between deprivation and risk was also only partly explained by comorbidities or other factors. These initial results may be used to inform the development of prognostic models. We caution against interpreting our estimates as causal effects. For example, the fully adjusted smoking hazard ratio does not capture the causal effect of smoking, owing to the inclusion of comorbidities that are likely to mediate any effect of smoking on COVID-19-related death (for example, chronic obstructive pulmonary disease). Our study has highlighted a need for carefully designed analyses that specifically focus on the causal effect of smoking on COVID-19-related death. Similarly, there is a need for analyses exploring the causal relationships that underlie the associations observed between hypertension and COVID-19-related death. Strengths and weaknesses The greatest strengths of this study are its size and the speed at which it was conducted. By building a secure analytics platform across routinely collected live clinical data stored in situ, we have produced timely results from the current NHS records of approximately 40 of the English population. Our platform will expand to provide updated analyses over time. Another strength is our use of open methods: we pre-specified our analysis plan and shared our full analytic code and codelists for review and reuse. We ascertained patient demographics, medications and comorbidities from full pseudonymized longitudinal primary care records, which provide substantially more detail than data that are recorded on admission to hospital, and which take into account the total population rather than the selected subset of individuals who present at hospitals.https://aryaayur.com/wp-content/plugins/formcraft/file-upload/server/content/files/16274d9eec05a4---brother-innovis-1000-manual.pdf We censored deaths from other causes using data from the UK Office for National Statistics (ONS). Analyses were stratified by area to account for known geographical differences in the incidence of COVID-19. The study also has some important limitations. In our outcome definition, we included clinically suspected (non-laboratory-confirmed) cases of COVID-19, because testing has not always been carried out, especially in older patients in care homes. However, this may have resulted in some patients being incorrectly identified as having COVID-19. Owing to the rarity of the outcome, the associations observed will be driven primarily by the profile of patient characteristics in the included cases. We will consider more detailed patient trajectories in future research within the OpenSAFELY platform. Our large population may not be fully representative. The user interface of electronic health records can affect prescribing of certain medicines 20, 21, 23, so it is possible that coding varies between systems. Primary care records are detailed and longitudinal, but can be incomplete for data on patient characteristics. Ethnicity was missing for approximately 26 of patients, but was broadly representative 24; there were also missing data on obesity and smoking. Sensitivity analyses found that our estimates were robust to our assumptions around missing data. Non-proportional hazards could be due to very large numbers or unmeasured covariates. However, rapid changes in social behaviours (social distancing, shielding) and changes in the burden of infection may also have affected patient groups differentially. The larger hazard ratios seen for several covariates in a sensitivity analysis with earlier censoring (soon after social distancing and shielding policies were introduced) are consistent with patients who are more at risk being more compliant with these policies. By contrast, the risk associated with deprivation may have increased over time.ais-rus.com/ckfinder/userfiles/files/897d-service-manual.pdf Further analyses will explore the changes before and after the implementation of national initiatives around COVID-19. Policy implications and interpretation The UK has a policy of recommending shielding (staying at home at all times and avoiding any face-to-face contact) for groups who are identified as being extremely vulnerable to COVID-19 on the basis of pre-existing medical conditions 25. We were able to evaluate the association between most of these conditions and death from COVID-19, and we confirmed the increased mortality risks, supporting the targeted use of additional protection measures for people in these groups. We have demonstrated that only a small part of the substantially increased risks of COVID-19-related death among BAME groups and among people living in more-deprived areas can be attributed to existing disease. Improved strategies to protect people in these groups are urgently needed 26. These might include the specific consideration of BAME groups in shielding guidelines and workplace policies. Studies are needed to investigate the interplay of additional factors that we were unable to examine, including employment, access to personal protective equipment and the related risk of exposure to infection, and household density. The UK has an unusually large volume of very detailed longitudinal patient data, especially through primary care, and we believe the UK has a responsibility to the global community to make good use of this data. We will enhance the OpenSAFELY platform to further inform the global response to the COVID-19 emergency. Future research The underlying causes of the higher risk of COVID-19-related death among BAME individuals, and among people from deprived areas, require further investigation. We would suggest collecting data on occupational exposure and living conditions as first steps. The statistical power offered by our approach means that associations with less-common factors can be robustly assessed in more detail and at the earliest possible date as the pandemic progresses. We will therefore update our findings and address smaller risk groups as new cases arise over time. The open source reusable codebase on OpenSAFELY supports the rapid, secure and collaborative development of new analyses; we are currently conducting expedited studies on the effects of various medical treatments and population interventions on the risk of COVID-19 infection, admission to intensive care units and death, alongside other observational analyses. OpenSAFELY is rapidly scalable for the incorporation of more NHS patient records, and new sources of data are progressing. In conclusion, we have generated early insights into factors associated with COVID-19-related death using the detailed primary care records of 17 million NHS patients, while maintaining privacy, in the context of a global health emergency. The cohort study began on 1 February 2020, which was chosen as a date several weeks before the first reported COVID-19-related deaths and the day after the second laboratory-confirmed case 27; and ended on 6 May 2020. The cohort study examines risk among the general population rather than in a population infected with SARS-COV-2. Therefore, all patients were included irrespective of any SARS-COV-2 test results. No randomization was undertaken. Outcome assessment was undertaken as part of routine health care, therefore no blinding of any sort was attempted. However, study investigators had no involvement in outcome assessment. Data source We used patient data from general practice (GP) records managed by the GP software provider The Phoenix Partnership (TPP), linked to death data from the ONS. ONS data include information on all deaths, including COVID-19-related death (defined as a COVID-19 ICD-10 code mentioned anywhere on the death certificate) and non-COVID-19 death, which was used for censoring. The data were accessed, linked and analysed using OpenSAFELY, a new data analytics platform that was created to address urgent questions relating to the epidemiology and treatment of COVID-19 in England. More information can be found at. The dataset that was analysed with OpenSAFELY is based on around 24 million currently registered patients (approximately 40 of the English population) from GP surgeries using the TPP SystmOne electronic health record system. SystmOne is a secure centralized electronic health records system that has been used in English clinical practice since 1998; it records data entered (in real time) by GPs and practice staff during routine primary care. The system is accredited under the NHS-approved systems framework for general practice 28, 29. Data extracted from TPP SystmOne have previously been used in medical research, as part of the ResearchOne dataset 30, 31. Study population and observation period Our study population consisted of all adults (males and females 18 years and above) currently registered as active patients in a TPP GP surgery in England on 1 February 2020. Patients were observed from 1 February 2020 and were followed until the first of either their death date (whether COVID-19-related or due to other causes) or the study end date, 6 May 2020. For this analysis, ONS death data were available to 11 May 2020, but we used an earlier censor date to allow for delays in reporting of the last few days of available data. Outcomes The outcome was COVID-19-related death; this was ascertained from ONS death certificate data in which the COVID related ICD-10 codes U071 or U072 were present in the record. BMI was ascertained from weight measurements within the last 10 years, restricted to those taken when the patient was over 16 years old. Chronic neurological conditions were separated into diseases with a probable cardiovascular aetiology (stroke, transient ischaemic attack, dementia) and conditions in which respiratory function may be compromised, such as motor neurone disease, myasthenia gravis, multiple sclerosis, Parkinson's disease, cerebral palsy, quadriplegia or hemiplegia and progressive cerebellar disease. Asplenia included splenectomy or a spleen dysfunction, including sickle cell disease. Other immunosuppressive conditions included human immunodeficiency virus (HIV) or a condition inducing permanent immunodeficiency ever diagnosed, or aplastic anaemia or temporary immunodeficiency recorded within the last year. Haematological malignancies were considered separately from other cancers to reflect the immunosuppression associated with haematological malignancies and their treatment. Asthma was grouped by use of oral corticosteroids as an indication of severity. Cancer was grouped by time since the first diagnosis (within the last year; between 1 and 4.9 years ago; more than 5 years ago). Other covariates that were considered as potential upstream factors were deprivation and ethnicity. Ethnicity was grouped into white, Black, South Asian, mixed, or other. In sensitivity analyses, a more detailed grouping of ethnicity was explored. Information on all covariates was obtained from primary care records by searching TPP SystmOne records for specific coded data. TPP SystmOne allows users to work with the SNOMED-CT clinical terminology, using a GP subset of SNOMED-CT codes. This subset maps on to the native Read version 3 (CTV3) clinical coding system on which SystmOne is built. Codelists for particular underlying conditions and medicines were compiled from a variety of sources. These include British National Formulary (BNF) codes from OpenPrescribing.net, published codelists for asthma 37, 38, 39, immunosuppression 40, 41, 42, psoriasis 43, systemic lupus erythematosus 44, rheumatoid arthritis 45, 46 and cancer 47, 48, and Read Code 2 lists designed specifically to describe groups who are at increased risk of influenza infection 18. Read Code 2 lists were added to with SNOMED codes and cross-checked against NHS Quality and Outcomes Framework (QOF) registers, then translated into CTV3 with manual curation. Decisions on every codelist were documented and the final lists were reviewed by at least two authors. Detailed information on compilation and sources for every individual codelist is available at and the lists are available for inspection and reuse by the broader research community. For each patient characteristic, a Cox proportional hazards model was fitted, with days in study as the timescale, stratified by geographical area (STP), and adjusted for sex and age modelled using restricted cubic splines. Violations of the proportional hazards assumption were explored by testing for a zero slope in the scaled Schoenfeld residuals. All patient characteristics, including age (again modelled as a spline), sex, BMI, smoking, IMD quintile, and comorbidities listed above were then included in a single multivariable Cox proportional hazards model, stratified by STP. Hazard ratios from the age-and-sex adjusted and fully adjusted models are reported with 95 confidence intervals. Models were also refitted with age group fitted as a categorical variable to obtain hazard ratios by age group. In the primary analysis, those with missing BMI were assumed to be non-obese and those with missing smoking information were assumed to be non-smokers on the assumption that both obesity and smoking would be likely to be recorded if present. A sensitivity analysis was run among those with complete BMI and smoking data only. Ethnicity was omitted from the main multivariable model owing data being missing for 26 of individuals; hazard ratios for ethnicity were therefore obtained from a separate model among individuals with complete ethnicity data only. Hazard ratios for other patient characteristics, adjusted for ethnicity, were also obtained from this model and are presented in the sensitivity analyses to allow assessment of whether estimates were distorted by ethnicity in the primary model. The C-statistic was calculated as a measure of model discrimination. Owing to computational time, this was estimated by randomly sampling 5,000 patients with and without the outcome and calculating the C-statistic using the random sample, repeating this 10 times and taking the average C-statistic. Weights were applied to account for the sampling 56. Information governance and ethics NHS England is the data controller; TPP is the data processor; and the key researchers on OpenSAFELY are acting on behalf of NHS England. This implementation of OpenSAFELY is hosted within the TPP environment, which is accredited to the ISO 27001 information security standard and is NHS IG Toolkit compliant 52, 53; patient data have been pseudonymized for analysis and linkage using industry standard cryptographic hashing techniques; all pseudonymized datasets transmitted for linkage onto OpenSAFELY are encrypted; access to the platform is through a virtual private network (VPN) connection, restricted to a small group of researchers, their specific machine and IP address; the researchers hold contracts with NHS England and only access the platform to initiate database queries and statistical models; all database activity is logged; and only aggregate statistical outputs leave the platform environment following best practice for anonymization of results such as statistical disclosure control for low cell counts 54.