103 research outputs found
Labour intensity of guidelines may have a greater effect on adherence than GPs' workload
Background: Physicians' heavy workload is often thought to jeopardise the quality of care and to
be a barrier to improving quality. The relationship between these has, however, rarely been
investigated. In this study quality of care is defined as care 'in accordance with professional
guidelines'. In this study we investigated whether GPs with a higher workload adhere less to
guidelines than those with a lower workload and whether guideline recommendations that require
a greater time investment are less adhered to than those that can save time.
Methods: Data were used from the Second Dutch National survey of General Practice (DNSGP-
2). This nationwide study was carried out between April 2000 and January 2002.
A multilevel logistic-regression analysis was conducted of 170,677 decisions made by GPs, referring
to 41 Guideline Adherence Indicators (GAIs), which were derived from 32 different guidelines.
Data were used from 130 GPs, working in 83 practices with 98,577 patients. GP-characteristics as
well as guideline characteristics were used as independent variables. Measures include workload
(number of contacts), hours spent on continuing medical education, satisfaction with available time,
practice characteristics and patient characteristics. Outcome measure is an indicator score, which
is 1 when a decision is in accordance with professional guidelines or 0 when the decision deviates
from guidelines.
Results: On average, 66% of the decisions GPs made were in accordance with guidelines. No
relationship was found between the objective workload of GPs and their adherence to guidelines.
Subjective workload (measured on a five point scale) was negatively related to guideline adherence
(OR = 0.95). After controlling for all other variables, the variation between GPs in adherence to
guideline recommendations showed a range of less than 10%.
84% of the variation in guideline adherence was located at the GAI-level. Which means that the
differences in adherence levels between guidelines are much larger than differences between GPs.
Guideline recommendations that require an extra time investment during the same consultation
are significantly less adhered to: (OR = 0.46), while those that can save time have much higher
adherence levels: OR = 1.55). Recommendations that reduce the likelihood of a follow-up consultation for the same problem are also more often adhered to compared to those that have
no influence on this (OR = 3.13).
Conclusion: No significant relationship was found between the objective workload of GPs and
adherence to guidelines. However, guideline recommendations that require an extra time
investment are significantly less well adhered to while those that can save time are significantly
more often adhered to.
Accurate evaluation of the interstitial KKR-Green function
It is shown that the Brillouin zone integral for the interstitial KKR-Green
function can be evaluated accurately by taking proper care of the free-electron
singularities in the integrand. The proposed method combines two recently
developed methods, a supermatrix method and a subtraction method. This
combination appears to provide a major improvement compared with an earlier
proposal based on the subtraction method only. By this the barrier preventing
the study of important interstitial-like defects, such as an electromigrating
atom halfway along its jump path, can be considered as being razed.Comment: 23 pages, RevTe
A spliced latency-associated VZV transcript maps antisense to the viral transactivator gene
Varicella-zoster virus (VZV), an alphaherpesvirus, establishes lifelong latent infection in the neurons of >90% humans worldwide, reactivating in one-third to cause shingles, debilitating pain and stroke. How VZV maintains latency remains unclear. Here, using ultra-deep virus-enriched RNA sequencing of latently infected human trigeminal ganglia (TG), we demonstrate the consistent expression of a spliced VZV mRNA, antisense to VZV open reading frame 61 (ORF61). The spliced VZV latency-associated transcript (VLT) is expressed in human TG neurons and encodes a protein with late kinetics in productively infected cells in vitro and in shingles skin lesions. Whereas multiple alternatively spliced VLT isoforms (VLTly) are expressed during lytic infection, a single unique VLT isoform, which specifically suppresses ORF61 gene expression in co-transfected cells, predominates in latently VZV-infected human TG. The discovery of VLT links VZV with the other better characterized human and animal neurotropic alphaherpesviruses and provides insights into VZV latency
Ranking of Fuzzy Similar Faces Using Relevance Matrix and Aggregation Operators
AbstractIn perception based imaging, Sketching With Words (SWW) is a well-established methodology in which the objects of computation are fuzzy geometric objects (f-objects).The problem of facial imaging of criminal on the basis of onlooker statement is not lack of method and measures but the modeling of onlooker(s) mind set. Because the onlooker has to give statements about different human face parts like forehead, eyes, nose, and chin etc.The concept of fuzzy similarity (f-similarity) and proper aggregation of components of face may provide more flexibility to onlooker(s). In proposed work onlooker(s) statement is recorded. Thereafter it is compared with existing statements. The f-similarity with different faces in database is estimated by using ‘as many as possible’ linguistic quantifier. Three types of constraints over size of parts of face ‘small’, ‘medium’, and ‘large’ are considered. Possibilistic constraints with linguistic hedges and negation operator like ‘very long’, ‘not long’, ‘not very long’ etc. are used. Moreover we have generated ranking of alike faces in decreasing order by using the concepts of f-similarity and relevance matrix
The FLAMINGO project: cosmological hydrodynamical simulations for large-scale structure and galaxy cluster surveys
We introduce the Virgo Consortium's FLAMINGO suite of hydrodynamical
simulations for cosmology and galaxy cluster physics. To ensure the simulations
are sufficiently realistic for studies of large-scale structure, the subgrid
prescriptions for stellar and AGN feedback are calibrated to the observed
low-redshift galaxy stellar mass function and cluster gas fractions. The
calibration is performed using machine learning, separately for three
resolutions. This approach enables specification of the model by the
observables to which they are calibrated. The calibration accounts for a number
of potential observational biases and for random errors in the observed stellar
masses. The two most demanding simulations have box sizes of 1.0 and 2.8 Gpc
and baryonic particle masses of and ,
respectively. For the latter resolution the suite includes 12 model variations
in a 1 Gpc box. There are 8 variations at fixed cosmology, including shifts in
the stellar mass function and/or the cluster gas fractions to which we
calibrate, and two alternative implementations of AGN feedback (thermal or
jets). The remaining 4 variations use the unmodified calibration data but
different cosmologies, including different neutrino masses. The 2.8 Gpc
simulation follows particles, making it the largest ever
hydrodynamical simulation run to . Lightcone output is produced on-the-fly
for up to 8 different observers. We investigate numerical convergence, show
that the simulations reproduce the calibration data, and compare with a number
of galaxy, cluster, and large-scale structure observations, finding very good
agreement with the data for converged predictions. Finally, by comparing
hydrodynamical and `dark-matter-only' simulations, we confirm that baryonic
effects can suppress the halo mass function and the matter power spectrum by up
to per cent.Comment: 44 pages, 23 figures. Accepted for publication in MNRAS. V3 includes
changes made in published version: jet simulations were redone to fix a bug,
but the differences are nearly invisible. For visualizations, see the
FLAMINGO website at https://flamingo.strw.leidenuniv.nl
FLAMINGO: Calibrating large cosmological hydrodynamical simulations with machine learning
To fully take advantage of the data provided by large-scale structure
surveys, we need to quantify the potential impact of baryonic effects, such as
feedback from active galactic nuclei (AGN) and star formation, on cosmological
observables. In simulations, feedback processes originate on scales that remain
unresolved. Therefore, they need to be sourced via subgrid models that contain
free parameters. We use machine learning to calibrate the AGN and stellar
feedback models for the FLAMINGO cosmological hydrodynamical simulations. Using
Gaussian process emulators trained on Latin hypercubes of 32 smaller-volume
simulations, we model how the galaxy stellar mass function and cluster gas
fractions change as a function of the subgrid parameters. The emulators are
then fit to observational data, allowing for the inclusion of potential
observational biases. We apply our method to the three different FLAMINGO
resolutions, spanning a factor of 64 in particle mass, recovering the observed
relations within the respective resolved mass ranges. We also use the
emulators, which link changes in subgrid parameters to changes in observables,
to find models that skirt or exceed the observationally allowed range for
cluster gas fractions and the stellar mass function. Our method enables us to
define model variations in terms of the data that they are calibrated to rather
than the values of specific subgrid parameters. This approach is useful,
because subgrid parameters are typically not directly linked to particular
observables, and predictions for a specific observable are influenced by
multiple subgrid parameters.Comment: 24 pages, 10 figures (Including the appendix). Submitted to MNRAS.
For visualisations, see the FLAMINGO website at
https://flamingo.strw.leidenuniv.nl
FLAMINGO: calibrating large cosmological hydrodynamical simulations with machine learning.
To fully take advantage of the data provided by large-scale structure surveys, we need to quantify the potential impact of baryonic effects, such as feedback from active galactic nuclei (AGN) and star formation, on cosmological observables. In simulations, feedback processes originate on scales that remain unresolved. Therefore, they need to be sourced via subgrid models that contain free parameters. We use machine learning to calibrate the AGN and stellar feedback models for the FLAMINGO (Fullhydro Large-scale structure simulations with All-sky Mapping for the Interpretation of Next Generation Observations) cosmological hydrodynamical simulations. Using Gaussian process emulators trained on Latin hypercubes of 32 smaller volume simulations, we model how the galaxy stellar mass function (SMF) and cluster gas fractions change as a function of the subgrid parameters. The emulators are then fit to observational data, allowing for the inclusion of potential observational biases. We apply our method to the three different FLAMINGO resolutions, spanning a factor of 64 in particle mass, recovering the observed relations within the respective resolved mass ranges. We also use the emulators, which link changes in subgrid parameters to changes in observables, to find models that skirt or exceed the observationally allowed range for cluster gas fractions and the SMF. Our method enables us to define model variations in terms of the data that they are calibrated to rather than the values of specific subgrid parameters. This approach is useful, because subgrid parameters are typically not directly linked to particular observables, and predictions for a specific observable are influenced by multiple subgrid parameters. [Abstract copyright: © 2023 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society.
Comparative Performance Information Plays No Role in the Referral Behaviour of GPs
Comparative performance information (CPI) about the quality of hospital care is information used to identify high-quality hospitals and providers. As the gatekeeper to secondary care, the general practitioner (GP) can use CPI to reflect on the pros and cons of the available options with the patient and choose a provider best fitted to the patient’s needs. We investigated how GPs view their role in using CPI to choose providers and support patients.
Method: We used a mixed-method, sequential, exploratory design to conduct explorative interviews with 15 GPs about their referral routines, methods of referral consideration, patient involvement, and the role of CPI. Then we quantified the qualitative results by sending a survey questionnaire to 81 GPs affiliated with a representative national research network.
Results: Seventy GPs (86% response rate) filled out the questionnaire. Most GPs did not know where to find CPI (87%) and had never searched for it (94%). The GPs reported that they were not motivated to use CPI due to doubts about its role as support information, uncertainty about the effect of using CPI, lack of faith in better outcomes, and uncertainty about CPI content and validity. Nonetheless, most GPs believed that patients would like to be informed about quality-of- care differences (62%), and about half the GPs discussed quality-of-care differences with their patients (46%), though these discussions were not based on CPI.
Conclusion: Decisions about referrals to hospital care are not based on CPI exchanges during GP consultations. As a gatekeeper, the GP is in a good position to guide patients through the enormous amount of quality information that is available. Nevertheless, it is unclear how and whether the GP’s role in using information about quality of care in the referral process can grow, as patients hardly ever initiate a discussion based on CPI, though they seem to be increasingly more critical about differences in quality of care. Future research should address the conditions needed to support GPs’ ability and willingness to use CPI to guide their patients in the referral process
Primary care nurses struggle with lifestyle counseling in diabetes care: a qualitative analysis
Contains fulltext :
89605.pdf (publisher's version ) (Open Access)BACKGROUND: Patient outcomes are poorly affected by lifestyle advice in general practice. Promoting lifestyle behavior change require that nurses shift from simple advice giving to a more counseling-based approach. The current study examines which barriers nurses encounter in lifestyle counseling to patients with type 2 diabetes. Based on this information we will develop an implementation strategy to improve lifestyle behavior change in general practice. METHOD: In a qualitative semi-structured study, twelve in-depth interviews took place with nurses in Dutch general practices involved in diabetes care. Specific barriers in counseling patients with type 2 diabetes about diet, physical activity, and smoking cessation were addressed. The nurses were invited to reflect on barriers at the patient and practice levels, but mainly on their own roles as counselors. All interviews were audio-recorded and transcribed. The data were analyzed with the aid of a predetermined framework. RESULTS: Nurses felt most barriers on the level of the patient; patients had limited knowledge of a healthy lifestyle and limited insight into their own behavior, and they lacked the motivation to modify their lifestyles or the discipline to maintain an improved lifestyle. Furthermore, nurses reported lack of counseling skills and insufficient time as barriers in effective lifestyle counseling. CONCLUSIONS: The traditional health education approach is still predominant in primary care of patients with type 2 diabetes. An implementation strategy based on motivational interviewing can help to overcome 'jumping ahead of the patient' and promotes skills in lifestyle behavioral change. We will train our nurses in agenda setting to structure the consultation based on prioritizing the behavior change and will help them to develop social maps that contain information on local exercise programs
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