Many capture-recapture surveys of wildlife populations operate in continuous time but detections are typically aggregated into occasions for analysis, even when exact detection times are available. This discards information and introduces subjectivity, in the form of decisions about occasion definition. We develop a spatio-temporal Poisson process model for spatially explicit capture-recapture (SECR) surveys that operate continuously and record exact detection times. We show that, except in some special cases (including the case in which detection probability does not change within occasion), temporally aggregated data do not provide sufficient statistics for density and related parameters, and that when detection probability is constant over time our continuous-time (CT) model is equivalent to an existing model based on detection frequencies. We use the model to estimate jaguar density from a camera-trap survey and conduct a simulation study to investigate the properties of a CT estimator and discrete-occasion estimators with various levels of temporal aggregation. This includes investigation of the effect on the estimators of spatio-temporal correlation induced by animal movement. The CT estimator is found to be unbiased and more precise than discrete-occasion estimators based on binary capture data (rather than detection frequencies) when there is no spatio-temporal correlation. It is also found to be only slightly biased when there is correlation induced by animal movement, and to be more robust to inadequate detector spacing, while discrete-occasion estimators with binary data can be sensitive to occasion length, particularly in the presence of inadequate detector spacing. Our model includes as a special case a discrete-occasion estimator based on detection frequencies, and at the same time lays a foundation for the development of more sophisticated CT models and estimators. It allows modelling within-occasion changes in detectability, readily accommodates variation in detector effort, removes subjectivity associated with user-defined occasions, and fully utilises CT data. We identify a need for developing CT methods that incorporate spatio-temporal dependence in detections and see potential for CT models being combined with telemetry-based animal movement models to provide a richer inference framework.

Conflict management is rarely explored among physiotherapists though they often work in teams. Hence, this study explored attitudes, perceived competencies, beliefs, training experiences, and needs in conflict management among Italian physiotherapists. We conducted a cross-sectional online survey study between June and September 2023 among Italian physiotherapists. The survey instrument comprised four sections. Section 1: Socio-Demographic and Professional Data: Explored participant profiles and conflict frequency. Section 2: Attitudes and Competences: assess conflict-related behaviours and management styles (Likert Scale). Section 3: Training Experiences and Needs: Evaluated training importance and conflict-related issues with other professionals (Likert Scale). Section 4: Beliefs About Factors: Participants rated (0–10) factors influencing conflict management and its impact on care and well-being. Descriptive analyses were performed, presenting continuous data as mean (SD) and categorical data as frequencies/percentages. Likert scale responses were dichotomised (agreement/disagreement), and consensus was defined as ≥70% agreement. Median, quartiles, and box-and-whisker plots depicted responses were used for 0-to-10 scales. Physiotherapists (n = 203; mean age: 39±10.40) generally leaned towards a constructive communication style, characterised by compromise and collaboration, viewing conflict management as an opportunity to grow. There was a disparity between their exhibited behaviours and self-assessment of appropriateness in conflict resolution. Only 27.6% considered their conflict resolution skills as satisfactory. However, 85.7% acknowledged the significance of being trained in conflict management. Challenges were evident in conflicts within interprofessional relationships and communication with superiors. Both personal and organisational factors were identified as influencing conflict management, with participants recognising the detrimental impact of conflicts on their well-being and patient care. This study highlighted educational gaps in conflict management among Italian physiotherapists, showing areas of improvement in their training. Our results suggested that physiotherapists might need additional training in conflict management to enhance workplace well-being and the quality of care provided.

A combination of discrete and daily-aligned groundwater levels for the Mississippi River Valley alluvial aquifer clipped to the Mississippi Alluvial Plain, as defined by Painter and Westerman (2018), with corresponding metadata are based on processing of U.S. Geological Survey National Water Information System (NWIS) (U.S. Geological Survey, 2020) data. The processing was made after retrieval using aggregation and filtering through the infoGW2visGWDB software (Asquith and Seanor, 2019). The nomenclature GWmaster mimics that of the output from infoGW2visGWDB. Two separate data retrievals for NWIS were made. First, the discrete data were retrieved, and second, continuous records from recorder sites with daily-mean or other daily statistics codes were retrieved. Each dataset was separately passed through the infoGW2visGWDB software to create a "GWmaster discrete" and "GWmaster continuous" and these tables were combined and then sorted on the site identifier and date to form the data products described herein. A sweep through the combined dataset (the "database") was made to isolate duplicate observations, or observations for the same well and on the same day. If a discrete value was present, it was retained as authoritative for the day and in descending order of priority daily-mean, daily-maximum, and daily minimum. Therefore, only a single record for a well and day are present in the dataset. The duplicate search removed 876 records and 31 wells were involved; in total, this is about 0.3 percent of the database. References: Asquith, W.H., Seanor, R.C., 2019, infoGW2visGWDB—An R groundwater data-processing utility for manipulating, checking the veracity, and converting an "infoGW" object to the "GWmaster" object for the visGWDB software with demonstration for the Mississippi River Valley alluvial aquifer: U.S. Geological Survey software release, Reston, Va., https://doi.org/10.5066/P9MK0B6L. Painter, J.A., and Westerman, D.A., 2018. Mississippi Alluvial Plain extent, November 2017: U.S. Geological Survey data release, https://doi.org/10.5066/F70R9NMJ. U.S. Geological Survey, 2020, USGS water data for the Nation: U.S. Geological Survey National Water Information System database, accessed April 2, 2020, at https://doi.org/10.5066/F7P55KJN.

In April 2011 a new set of clinical quality indicators was introduced to replace the previous four hour waiting time standard, and measure the quality of care delivered in A&E departments in England. Further details on the background and management of the quality indicators are available from the Department of Health (DH) website. This is the eighth publication of data on the Accident and Emergency (A&E) clinical quality indicators, drawn from A&E data within provisional Hospital Episode Statistics (HES). These data relate to A&E attendances in November 2011 and draw on 1.36 million detailed records of attendances at major A&E departments, single speciality A&E departments (e.g. dental A&Es), minor injury units and walk-in centres in England. This report sets out data coverage, data quality and performance information for the following 5 A&E indicators: Left department before being seen for treatment rate Re-attendance rate Time to initial assessment Time to treatment Total time in A&E Publishing these data will help share information on the quality of care of A&E services to stimulate the discussion and debate between patients, clinicians, providers and commissioners, which is needed in a culture of continuous improvement. These A&E HES data are published as experimental statistics to note the shortfalls in the quality and coverage of records submitted via the A&E commissioning data set. The data used in these reports are sourced from Provisional A&E HES data, and as such these data may differ to information extracted directly from Secondary Uses Service (SUS) data, or data extracted directly from local patient administration systems. Provisional HES data may be revised throughout the year (for example, activity data for April 2011 may differ depending on whether they are extracted in August 2011, or later in the year). Indicator data published for earlier months have not been revised using updated HES data extracted in subsequent months. The data presented here represent the output of the existing A&E Commissioning Dataset (CDS V6 Type 010). It must be recognised that these data will not exactly match the data definitions for the A&E clinical quality indicators set out in the guidance document A&E clinical quality indicators: Implementation guidance and data definitions (DH website). The DH is currently working with Information Standards Board to amend the existing CDS Type 10 Accident and Emergency to collect the data required to monitor the A&E indicators. A&E HES data are collected and published by the NHS Information Centre for Health and Social Care. The data in this report are secondary analyses of HES data produced by the Urgent & Emergency Care team, Department of Health. A&E HES data are published as experimental statistics to note the known shortfalls in the quality of some A&E HES data elements. The published information sets out where data quality for the indicators may be improved by, for example, reducing the number of unknown values (e.g. unknown times to initial assessment) and default values (e.g. the number of attendances that are automatically given a time to initial assessment of midnight 00:00). The quality and coverage of A&E HES data have considerably improved over the years, and the Department and the NHS Information Centre are working with NHS Performance and Information directors to further improve the data.

Evaluation Criteria (100 Points):

Define Problem Statement and perform Exploratory Data Analysis (10 points) Definition of problem (as per given problem statement with additional views) Observations on shape of data, data types of all the attributes, conversion of categorical attributes to 'category' (If required) , missing value detection, statistical summary. Univariate Analysis (distribution plots of all the continuous variable(s) barplots/countplots of all the categorical variables) Bivariate Analysis (Relationships between important variables such as workday and count, season and count, weather and count. Illustrate the insights based on EDA Comments on range of attributes, outliers of various attributes Comments on the distribution of the variables and relationship between them Comments for each univariate and bivariate plots Data Preprocessing (10 Points) Duplicate value check Missing value treatment Outlier treatment Feature engineering Data preparation for modeling Model building (10 Points) Build the Linear Regression model and comment on the model statistics Display model coefficients with column names Try out Ridge and Lasso regression Testing the assumptions of the linear regression model (50 Points) Multicollinearity check by VIF score (variables are dropped one-by-one till none has VIF>5) (10 Points) The mean of residuals is nearly zero (10 Points) Linearity of variables (no pattern in the residual plot) (10 Points) Test for Homoscedasticity (10 Points) Normality of residuals (almost bell-shaped curve in residuals distribution, points in QQ plot are almost all on the line) (10 Points) Model performance evaluation (10 Points) Metrics checked - MAE, RMSE, R2, Adj R2 Train and test performances are checked Comments on the performance measures and if there is any need to improve the model or not Actionable Insights & Recommendations (10 Points) Comments on significance of predictor variables Comments on additional data sources for model improvement, model implementation in real world, potential business benefits from improving the model (These are key to differentiating a good and an excellent solution)

Submission Process:

Type your insights and recommendations in the text editor. Convert your jupyter notebook into PDF (Save as PDF using Chrome browser’s Print command), upload it on our platform Optionally, you may add images/graphs in the text editor by taking screenshots or saving matplotlib graphs using plt.savefig(...). After submitting, you will not be allowed to edit your submission.

This dataset contains customer satisfaction scores collected from a survey, alongside key demographic and behavioral data. It includes variables such as customer age, gender, location, purchase history, support contact status, loyalty level, and satisfaction factors. The dataset is designed to help analyze customer satisfaction, identify trends, and develop insights that can drive business decisions.

File Information: File Name: customer_satisfaction_data.csv (or your specific file name)

File Type: CSV (or the actual file format you are using)

Number of Rows: 120

Number of Columns: 10

Column Names:

Customer_ID – Unique identifier for each customer (e.g., 81-237-4704)

Group – The group to which the customer belongs (A or B)

Satisfaction_Score – Customer's satisfaction score on a scale of 1-10

Age – Age of the customer

Gender – Gender of the customer (Male, Female)

Location – Customer's location (e.g., Phoenix.AZ, Los Angeles.CA)

Purchase_History – Whether the customer has made a purchase (Yes or No)

Support_Contacted – Whether the customer has contacted support (Yes or No)

Loyalty_Level – Customer's loyalty level (Low, Medium, High)

Satisfaction_Factor – Primary factor contributing to customer satisfaction (e.g., Price, Product Quality)

Statistical Analyses:

Descriptive Statistics:

Calculate mean, median, mode, standard deviation, and range for key numerical variables (e.g., Satisfaction Score, Age).

Summarize categorical variables (e.g., Gender, Loyalty Level, Purchase History) with frequency distributions and percentages.

Two-Sample t-Test (Independent t-test):

Compare the mean satisfaction scores between two independent groups (e.g., Group A vs. Group B) to determine if there is a significant difference in their average satisfaction scores.

Paired t-Test:

If there are two related measurements (e.g., satisfaction scores before and after a certain event), you can compare the means using a paired t-test.

One-Way ANOVA (Analysis of Variance):

Test if there are significant differences in mean satisfaction scores across more than two groups (e.g., comparing the mean satisfaction score across different Loyalty Levels).

Chi-Square Test for Independence:

Examine the relationship between two categorical variables (e.g., Gender vs. Purchase History or Loyalty Level vs. Support Contacted) to determine if there’s a significant association.

Mann-Whitney U Test:

For non-normally distributed data, use this test to compare satisfaction scores between two independent groups (e.g., Group A vs. Group B) to see if their distributions differ significantly.

Kruskal-Wallis Test:

Similar to ANOVA, but used for non-normally distributed data. This test can compare the median satisfaction scores across multiple groups (e.g., comparing satisfaction scores across Loyalty Levels or Satisfaction Factors).

Spearman’s Rank Correlation:

Test for a monotonic relationship between two ordinal or continuous variables (e.g., Age vs. Satisfaction Score or Satisfaction Score vs. Loyalty Level).

Regression Analysis:

Linear Regression: Model the relationship between a continuous dependent variable (e.g., Satisfaction Score) and independent variables (e.g., Age, Gender, Loyalty Level).

Logistic Regression: If analyzing binary outcomes (e.g., Purchase History or Support Contacted), you could model the probability of an outcome based on predictors.

Factor Analysis:

To identify underlying patterns or groups in customer behavior or satisfaction factors, you can apply Factor Analysis to reduce the dimensionality of the dataset and group similar variables.

Cluster Analysis:

Use K-Means Clustering or Hierarchical Clustering to group customers based on similarity in their satisfaction scores and other features (e.g., Loyalty Level, Purchase History).

Confidence Intervals:

Calculate confidence intervals for the mean of satisfaction scores or any other metric to estimate the range in which the true population mean might lie.

This code analyses behavioural data from a group of 16 Parkinson patients and 15 healthy control participants performing an action adaptation tasks, in which participants need to continuously adapt the applied force based on the feedback they receive. The first feedback ranges from 0 (worst) to 10 (best) points depending on the error between actual force and target force (Value-cue) and the second feedback indicates whether the force had been too low or too high (Direction-feedback). The main behavioural outcomes are measures of force production and force adaptation (folder 1, used for figure 1 in the published article). In patients local field potentials were recorded during the task and corresponding code is stored in folder 2 (figure 2&3). In 14 patients burst deep brain stimulation was applied during a second session. Its effects on behaviour and local field potentials are analysed with code from folder 3 and 4 (figures 4&5). The results have been published in a paper entitled ‘Neural underpinnings of action adaptation in the subthalamic nucleus’ by Herz et al.

The code has been tested on a MacBook Pro, macOS Mojave 10.14.6. All data were analysed in Matlab (2019a, requires a software license) and FieldTrip. Installation guides can be found on: https://matlab.mathworks.com/ and https://www.fieldtriptoolbox.org/download/. Run times of the different scripts is usually short (from < 1 minutes to ~ 5 minutes) for most analyses except for cluster-based permutation tests of linear mixed effects models, which take a few hours.

Example data is provided for the behavioural analysis for 2 healthy control (HC) participants. Of note, this is not the actual data from HC01 & HC02 from the study, but it allows testing the behavioural scripts if applicable (see below for instructions).

(i) Behavioral data:

Scripts: ‘CompareLevodopaDemographicsMVC’: this script compares demographics and the maximum voluntary contraction (MVC) between patients and HC, and tests the effect of levodopa on the Unified Parkinson’s Disease Rating Scale (UPDRS). ‘GetEvents’ (_PD & _HC): Imports the events file from PsychoPy using ‘ExtractData’ (& _HC) and saves it as a mat-file. ‘GetForce’ (_PD & _HC): Computes several variables reflecting force production and adaptation using several functions: ‘Forceparameters’ computes measures of force production and the time of peak force, and allows illustrating single trials, ‘Forces_within’ computes mean and standard error of mean (SEM) of single subject force traces, ’Stat_within’ computes several single subject correlations and measures of force adaptation, ‘Forces_across’ computes mean and SEM of group force traces, ‘stat_across’ computes group-mean trajectories of actual force and target force. The results are saved as a mat-file for subject-averaged data and a csv-file for single subject data. ‘Plot_Stats’ computes statistics of these measures of force production and adaptation and plots the results.

(ii) Local field potential data:

Scripts: ‘GetLFP_FirstLevel.m’: Loads data and applies preprocessing, time-frequency analysis and re-aligning of data using FieldTrip. It uses the custom-written functions. ‘MakeMontage_AllBipolar’ (which creates a bipolar montage from the monopolar data) and ‘EpochData_TF’ (which epochs the continuous data aligned to the feedback cue and peak force). The epoched spectra are saved. ‘GetLFP_SecondLevel_PlotSpectra.m’: Loads the spectra from first level analysis and plots the grand average as well as group-averaged beta, alpha (for feedback-aligned data) and gamma traces (for movement-aligned data).
‘GetLFP_SecondLevel_LME.m’: Loads the spectra from first level analysis and computes LME analyses with variables of interest using moving windows of single trial beta power.For cluster-based permutation tests (which take several hours) the function ‘PermTests_LME’ is used. ‘GetLFP_SecondLevel_controlLME.m’: Loads the spectra from first level analysis and computes control LME analyses: Effect of Value and Direction on Alpha power in the feedback period (where it showed an increase), effect of change in force and absolute change in force on Gamma power before peak force (where it showed an increase) and on Beta power after the Value feedback (where it showed a correlation with Value).

(iii) DBS effects on behaviour:

Scripts: ‘GetEvents_Stim’: Imports the events file from PsychoPy using ‘ExtractData’ and saves it as a mat-file. ‘GetForce_Stim’: Computes several variables reflecting force production and adaptation using several functions: ‘Forceparameters’ computes measures of force production and the time of peak force, and allows illustrating single trials, ‘Forces_within’ computes mean and standard error of mean (SEM) of single subject force traces, ‘Forces_across’ computes mean and SEM of group force traces. The results are saved as a mat-file for subject-averaged data and a csv-file for single subject data. ‘GetToS’ loads a file with the stimulation trace during the task, calls the function ‘ToS_DownsampleBinaryRemoveRamp’ (which downsamples the data to 1000Hz, makes stimulation binary (1 for ON, 0 for OFF) and removes the ramping so that only stimulation at effective intensities counts as stimulation) and loads the relevant behavioural data (change in force and absolute change in force). It then calls the functions ‘ToS_WindowedStim’ (which computes for each trial whether or not stimulation was given in any 100 ms moving windows for cue- and movement aligned data) and ‘ToS_Windowed_nexttrial’ (which computes change in force and absolute change in force for windows in which stimulation was applied vs. was not applied).The results are saved in a mat-file. ‘Plot_ToS’ loads this data, plots effects of stimulation on absolute change in force and change in force and provides statistics using cluster-based permutation tests (‘PermTests_ToS’). It also saves single trial behavioural data with a column stating whether DBS was applied in the critical time windows (which is used for the DBS effects on local field potentials analysis).

(iv) DBS effects on local field potentials:

Scripts: ‘GetLFP_FirstLevel_Stim.m’: Loads data and applies preprocessing, time-frequency analysis and re-aligning of data using FieldTrip analogously to the script described under (ii) except that it also detrends and demeans the data, applies a low-pass filter at 100 Hz and excludes noisy data points, which are then interpolated. The epoched spectra aligned to feedback and movement are saved.

‘GetLFP_FirstLevel_Stim_TrigOnset.m’: Same as above, but aligned to onset of stimulation bursts.

‘GetLFP_SecondLevel_Stim.m’: Loads data from the previous analysis, loads single trial data with info whether DBS was applied at critical time windows and plots these beta traces together with beta power off stimulation for time windows of interest. Cluster-based permutation tests are applied using the function ‘PermTests_ToS’. Grand-average spectra and beta power irrespective of stimulation-timing are also plotted.

‘GetLFP_SecondLevel_TrigOnset.m’: Loads data from the previous _TrigOnset analysis and plots the group average.

(v) Downloaded scripts:

The following scripts were downloaded from mathworks.com: ‘computeCohen_d’ (measure of effect size), ‘jblill’ (filling significant clusters from permutation tests), ‘shadedErrorBar’ (illustrating mean and SEM).

(vi) Testing example data:

Two example datasets are provided (termed Kont01 & Kont02), which allow testing the behavioural force analysis. To do this the script GetForce_HC.m should be opened. DirName and EventPath should be adjusted for the actual path. Line 24: Should be changed to ‘for Subj=1:2’ Lines 103-108 should be commented, i.e. not used. Setting plotforce to 1 (line 12) plots the single subject and group average force spectra. Setting check to 1 (line 11) plots single trial force data. For this only use subject 1 or 2, not both (i.e. in line 24 use ‘for Subj=1’ or ‘for Subj=2’).

In April 2011 a new set of clinical quality indicators was introduced to replace the previous four hour waiting time standard, and measure the quality of care delivered in A&E departments in England. Further details on the background and management of the quality indicators are available from the Department of Health (DH) website. This is the second publication of data on the Accident and Emergency (A&E) clinical quality indicators, drawn from A&E data within provisional Hospital Episode Statistics (HES). These data relate to A&E attendances in May 2011 and draw on just over 1.4 million detailed records of attendances at major A&E departments, single speciality A&E departments (e.g. dental A&Es), minor injury units and walk-in centres in England. This report sets out data coverage, data quality and performance information for the following 5 A&E indicators: Left department before being seen for treatment rate Re-attendance rate Time to initial assessment Time to treatment Total time in A&E Publishing these data will help share information on the quality of care of A&E services to stimulate the discussion and debate between patients, clinicians, providers and commissioners, which is needed in a culture of continuous improvement. These A&E HES data are published as experimental statistics to note the shortfalls in the quality and coverage of records submitted via the A&E commissioning data set. The data used in these reports are sourced from Provisional A&E HES data, and as such these data may differ to information extracted directly from Secondary Uses Service (SUS) data, or data extracted directly from local patient administration systems. Provisional HES data may be revised throughout the year (for example, activity data for April 2011 may differ depending on whether they are extracted in August 2011, or later in the year). Indicator data published for earlier months have not been revised using updated HES data extracted in subsequent months. The data presented here represent the output of the existing A&E Commissioning Dataset (CDS V6 Type 010). It must be recognised that these data will not exactly match the data definitions for the A&E clinical quality indicators set out in the guidance document A&E clinical quality indicators: Implementation guidance and data definitions (external link). The DH is currently working with Information Standards Board to amend the existing CDS Type 10 Accident and Emergency to collect the data required to monitor the A&E indicators. A&E HES data are collected and published by the NHS Information Centre for Health and Social Care. The data in this report are secondary analyses of HES data produced by the Urgent & Emergency Care team, Department of Health. A&E HES data are published as experimental statistics to note the known shortfalls in the quality of some A&E HES data elements. The published information sets out where data quality for the indicators may be improved by, for example, reducing the number of unknown values (e.g. unknown times to initial assessment) and default values (e.g. the number of attendances that are automatically given a time to initial assessment of midnight 00:00). The quality and coverage of A&E HES data have considerably improved over the years, and the Department and the NHS Information Centre are working with NHS Performance and Information directors to further improve the data.

Based on Chinese General Social Survey data (CGSS 2021), binary logistic regression and stepwise regression were used to explore how Internet use improves the physical and mental health of elderly people and its influence mechanisms. The research found that Internet use has a positive and significant impact on the physical and mental health of the Chinese elderly, and the results are robust with variable replacement and model replacement tests. In its influence mechanism, it found that Internet use promotes the physical and mental health of elderly people through physical exercise, social interaction, and learning frequency, which have a partial mediating effect. The effectiveness of the Internet use in promoting physical and mental health of the Chinese elderly through learning frequency is higher than physical exercise and social interaction, highlighting the importance of continuous learning for the Chinese elderly in the digital age. At the same time, Internet use has an unequal influence on the physical and mental health of the Chinese elderly, and has a greater influence on the mental health of the elderly with higher socio-economic status. Therefore, the research proposes the following three suggestions. First, improve the popularity of Internet use among the Chinese elderly. Second, accelerate the development of Internet application products suitable for the Chinese elderly. Third, provide Internet education for different regions elderly groups, and implement targeted assistance for elderly people with poor socio-economic status.

ObjectiveThe aim of this study was to evaluate the impact of several employment-related aspects on overall job satisfaction among pharmacists working in Saudi pharmacy settings.MethodsA cross-sectional survey was conducted for a period of 1-month (December 2020) among pharmacists working in community pharmacies located in 3 cities of Saudi Arabia. Convenience sampling was employed, and the data was collected using the English version of Job Satisfaction Survey (JSS) questionnaire. The data was analyzed using IBM SPSS version 23. Descriptive statistics such as mean () and 95% confidence interval range were used to report continuous data; frequency (%) and sample counts (N) were used to report categorical data. Bivariate analyses were conducted using chi square (χ2) test. A multiple linear regression model was formulated to report the employment aspects that determined overall job satisfaction of pharmacists. The study was approved by an ethics committee.ResultsA total of 241 samples were analyzed. Less than a quarter of pharmacists (N = 54, 22.4%) were satisfied with their job. The overall job satisfaction score was 130.74 out of 199. The sub-scales for co-workers and communication had scores > 15.8 out of 24; subscale for operating conditions had score > 12.5 out of 20. The subscales for promotion and rewards had scores < 14 out of 24. The aspects of communication, fringe benefits and nature of work had the highest contribution towards overall job satisfaction. For a unit increase in score for communication, fringe benefits, and nature of work, the overall job satisfaction score increased by 0.204, 0.2, and 0.199, respectively.ConclusionA very small number of pharmacists seemed satisfied with their job. Satisfaction with communication, nature of work and fringe benefits contributed the most toward overall job satisfaction. Results of this study could provide the means for human resource managers and organizational policy makers to delve into the determinants of satisfaction among pharmacists working in community settings.

Background characteristics of the study variables (N = 241).

In April 2011 a new set of clinical quality indicators was introduced to replace the previous four hour waiting time standard, and measure the quality of care delivered in A&E departments in England. Further details on the background and management of the quality indicators are available from the Department of Health (DH) website. This is the fourth publication of data on the Accident and Emergency (A&E) clinical quality indicators, drawn from A&E data within provisional Hospital Episode Statistics (HES). These data relate to A&E attendances in July 2011 and draw on 1.4 million detailed records of attendances at major A&E departments, single speciality A&E departments (e.g. dental A&Es), minor injury units and walk-in centres in England. This report sets out data coverage, data quality and performance information for the following 5 A&E indicators: Left department before being seen for treatment rate Re-attendance rate Time to initial assessment Time to treatment Total time in A&E Publishing these data will help share information on the quality of care of A&E services to stimulate the discussion and debate between patients, clinicians, providers and commissioners, which is needed in a culture of continuous improvement. These A&E HES data are published as experimental statistics to note the shortfalls in the quality and coverage of records submitted via the A&E commissioning data set. The data used in these reports are sourced from Provisional A&E HES data, and as such these data may differ to information extracted directly from Secondary Uses Service (SUS) data, or data extracted directly from local patient administration systems. Provisional HES data may be revised throughout the year (for example, activity data for April 2011 may differ depending on whether they are extracted in August 2011, or later in the year). Indicator data published for earlier months have not been revised using updated HES data extracted in subsequent months. The data presented here represent the output of the existing A&E Commissioning Dataset (CDS V6 Type 010). It must be recognised that these data will not exactly match the data definitions for the A&E clinical quality indicators set out in the guidance document A&E clinical quality indicators: Implementation guidance and data definitions. The DH is currently working with Information Standards Board to amend the existing CDS Type 10 Accident and Emergency to collect the data required to monitor the A&E indicators. A&E HES data are collected and published by the NHS Information Centre. The data in this report are secondary analyses of HES data produced by the Urgent & Emergency Care team, Department of Health. A&E HES data are published as experimental statistics to note the known shortfalls in the quality of some A&E HES data elements. The published information sets out where data quality for the indicators may be improved by, for example, reducing the number of unknown values (e.g. unknown times to initial assessment) and default values (e.g. the number of attendances that are automatically given a time to initial assessment of midnight 00:00). The quality and coverage of A&E HES data have considerably improved over the years, and the Department and the NHS Information Centre are working with NHS Performance and Information directors to further improve the data.

In April 2011 a new set of clinical quality indicators was introduced to replace the previous four hour waiting time standard, and measure the quality of care delivered in A&E departments in England. Further details on the background and management of the quality indicators are available from the Department of Health (DH) website. This is the publication of data on the Accident and Emergency (A&E) clinical quality indicators, drawn from A&E data within provisional Hospital Episode Statistics (HES). These data relate to A&E attendances in March 2012 and draw on 1.53 million detailed records of attendances at major A&E departments, single speciality A&E departments (e.g. dental A&Es), minor injury units and walk-in centres in England. This report sets out data coverage, data quality and performance information for the following five A&E indicators: Left department before being seen for treatment rate Re-attendance rate Time to initial assessment Time to treatment Total time in A&E Publishing these data will help share information on the quality of care of A&E services to stimulate the discussion and debate between patients, clinicians, providers and commissioners, which is needed in a culture of continuous improvement. These A&E HES data are published as experimental statistics to note the shortfalls in the quality and coverage of records submitted via the A&E commissioning data set. The data used in these reports are sourced from Provisional A&E HES data, and as such these data may differ to information extracted directly from Secondary Uses Service (SUS) data, or data extracted directly from local patient administration systems. Provisional HES data may be revised throughout the year (for example, activity data for April 2011 may differ depending on whether they are extracted in August 2011, or later in the year). Indicator data published for earlier months have not been revised using updated HES data extracted in subsequent months. The data presented here represent the output of the existing A&E Commissioning Dataset (CDS V6 Type 010). It must be recognised that these data will not exactly match the data definitions for the A&E clinical quality indicators set out in the guidance document A&E clinical quality indicators: Implementation guidance and data definitions (external link). The DH is currently working with Information Standards Board to amend the existing CDS Type 10 Accident and Emergency to collect the data required to monitor the A&E indicators. A&E HES data are collected and published by the NHS Information Centre for Health and Social Care. The data in this report are secondary analyses of HES data produced by the Urgent & Emergency Care team, Department of Health. A&E HES data are published as experimental statistics to note the known shortfalls in the quality of some A&E HES data elements. The published information sets out where data quality for the indicators may be improved by, for example, reducing the number of unknown values (e.g. unknown times to initial assessment) and default values (e.g. the number of attendances that are automatically given a time to initial assessment of midnight 00:00). The quality and coverage of A&E HES data have considerably improved over the years, and the Department and the NHS Information Centre are working with NHS Performance and Information directors to further improve the data.