Business in the Community

There is limited understanding about the end-of-life experiences of intersectionally marginalised groups. A potential way to support greater access and inclusion of such individuals in research is to have community researchers as part of the core research team. Community researchers here are defined as individuals with similar social identities, heritage and location to the research participants of interest.However, how best to work with community researchers in the context of structural inequalities at the end-of-life remains poorly understood.AimWe aimed to elicit and test programme theories on how to effectively work with community researchers in end-of-life care research focussing on intersectional inequalities.MethodsA rapid realist review and evaluation were conducted. The review elicited programme theories to understand what works for whom, under what circumstances, how and why (Context-Mechanism-Outcome). These theories were then evaluated through a 12-month feasibility study where three Pakistani women from a structurally disadvantaged and ethnically diverse UK city formed part of a research team led by two women from a similar background.ResultsThree overarching programme theories were developed including (i) how power was structured, acknowledged and navigated, (ii) mechanisms of inclusion of the community researchers and participants, (iii) importance of reciprocal relationships and the tensions this posed with academic expectations, ethics procedures and structures.ConclusionCommunity researchers are key assets to enable better and safer access and inclusion of intersectionally marginalised individuals in end-of-life care research. However, addressing evolving power imbalances at multiple levels is essential to mitigate against the extractive nature of research practice.ImpactBy centring power sharing, inclusion and relationships, trust can be built in a manner that re-humanises research practice. This improves not only the quality of inequalities research but also the well-being of the entire research team. The need for adequate resources to enable this is essential.

The coronavirus disease 2019 (COVID-19) had a worldwide impact on all aspects of life and primarily on clinical practice involving all health-care professionals. Pharmacists are at the forefront of all clinical facilities in hospitals and the community. We aimed to provide a narrative overview of the global literature on the impact of COVID-19 changing roles and pharmacists' contribution to the overall clinical care during the COVID-19 pandemic. We conducted a narrative, nonsystematic review of the international literature from a single major medical online database, PubMed, between February 1, 2020, and June 30, 2021. The relevant articles were narrated in a concise thematic account. During the COVID-19 pandemic, pharmacists had to serve their responsibilities alongside other activities while the entire world was fighting an invisible virus. However, it was much more complicated than before because the novel coronavirus is intractable. There was no vaccine or established therapeutic guidelines when it manifested suddenly. As part of an interdisciplinary team, community pharmacists may carry out their responsibilities and play an essential role in disease control and management. However, they may need to be appropriately prepared, trained, and equipped to deal with pandemic and postpandemic situations.

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Abstract The COVID-19 pandemic has strained testing capabilities worldwide. There is an urgent need to find economical and scalable ways to test more people. We presentTapestry, a novel quantitative nonadaptive pooling scheme to test many samples using only a few tests. The underlying molecular diagnostic test is any real-time RT-PCR diagnostic panel approved for the detection of the SARS-CoV-2 virus. In cases where most samples are negative for the virus, Tapestry accurately identifies the status of each individual sample with a single round of testing in fewer tests than simple two-round pooling. We also present a companion Android applicationBYOM Smart Testingwhich guides users through the pipetting steps required to perform the combinatorial pooling. The results of the pooled tests can be fed into the application to recover the status and estimated viral load for each individual sample.NOTE: This protocol has been validated with in vitro experiments that used synthetic RNA and DNA fragments and additionally, its expected behavior has been confirmed using computer simulations. Validation with clinical samples is ongoing. We are looking for clinical collaborators with access to patient samples. Please contact the corresponding author if you wish to validate this protocol on clinical samples.

Bugfix for PyPI

New features <ul> <li>Ability to create custom parametric regression models by specifying the cumulative hazard. This enables new and extensions of AFT models.</li> <li><code>percentile(p)</code> method added to univariate models that solves the equation <code>p = S(t)</code> for <code>t</code></li> <li>for parametric univariate models, the <code>conditional_time_to_event_</code> is now exact instead of an approximation.</li> </ul> API changes <ul> <li>In Cox models, the attribute <code>hazards_</code> has been renamed to <code>params_</code>. This aligns better with the other regression models, and is more clear (what is a hazard anyways?)</li> <li>In Cox models, a new <code>hazard_ratios_</code> attribute is available which is the exponentiation of <code>params_</code>.</li> <li>In regression models, the column names in <code>confidence_intervals_</code> has changed to include the alpha value.</li> <li>In regression models, some column names in <code>.summary</code> and <code>.print_summary</code> has changed to include the alpha value.</li> <li>In regression models, some column names in <code>.summary</code> and <code>.print_summary</code> includes confidence intervals for the exponential of the value.</li> <li>Significant changes to internal AFT code.</li> <li>A change to how <code>fit_intercept</code> works in AFT models. Previously one could set <code>fit_intercept</code> to False and not have to set <code>ancillary_df</code> - now one must specify a DataFrame.</li> </ul> Bug fixes <ul> <li>for parametric univariate models, the <code>conditional_time_to_event_</code> is now exact instead of an approximation.</li> </ul>

0.21.3 - 2019-06-04 New features include in lifelines is a scikit-learn adapter so lifeline's models can be used with scikit-learn's API. See documentation here. <code>CoxPHFitter.plot</code> now accepts a <code>hazard_ratios</code> (boolean) parameter that will plot the hazard ratios (and CIs) instead of the log-hazard ratios. <code>CoxPHFitter.check_assumptions</code> now accepts a <code>columns</code> parameter to specify only checking a subset of columns. Bug fixes <code>covariates_from_event_matrix</code> handle nulls better