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Conclusions Most PSC survey results focus solely on climate level. To facilitate improvement in PSC, we advocate a simple, holistic safety climate profile including three metrics: climate level (using mean or per cent positive climate scores), climate strength (using the Rwg(j), or SD as a proxy) and the shape of the distribution (using histograms to see the distribution of scores within units). In PSC research, we advocate paying attention to climate strength as an important variable in its own right. Focusing on PSC level and strength can further understanding of the extent to which PSC is a key variable in the domain of patient safety.

Patient safety climate (PSC) remains important and problematic, and we continue to struggle with how to improve it. In part, this is because practitioners and researchers alike are examining it incompletely. By and large, the PSC literature defines PSC as shared perceptions among group members concerning the procedures, practices and kinds of behaviours that get rewarded and supported with regard to patient safety (PS). When defined this way, group-level climate is the focus (eg, data collected from 20 nurses in one intensive care unit (ICU) may be averaged to create an ICU safety climate score) and measuring and reporting safety climate perceptions requires group-level analysis.

There is a well-developed body of organisational literature on team composition models that speaks to how a construct at one level, such as individual-level climate perceptions gathered using a survey, is related to that construct at another level (ie, group climate). There are competing views as to whether a consensus or a dispersion model should be used to measure climate. In a direct consensus model, PSC is conceptualised as a shared team property—a common perception of safety among all the members of a department.

Consensus or agreement among the individuals in a group is in fact considered a prerequisite to accurately measuring the unit/group climate: most members of the group must agree in rating the safety climate as poor, average or excellent in order to describe the unit as having a negative, neutral or positive climate in the first place. Rwg(j) is a measure of within-group agreement that ranges from 0 (indicating no agreement) to 1 (indicating perfect agreement).

It shows the extent to which members of a work unit provide the exact same numerical rating on the questions of a PSC survey. In the direct consensus model, sufficient levels of within-group agreement (typically measured using the Rwg(j) ) and between-group variability (typically measured with intraclass correlation coefficient (ICC)(1)) are required before individual-level survey data on climate perceptions can be aggregated to and analysed at the group level. Pc Roset Kawasaki Download Itunes on this page.

Aggregation typically occurs by simply averaging climate scores reported by the unit's members. As noted, the PSC literature has largely taken this consensus approach. In dispersion models, on the other hand, variability among individuals is the focal construct. Variability refers to the degree of disagreement among members regarding their unit's safety orientation. Schneider et al argue that variability or dispersion itself has received limited attention (or has been treated as a ‘statistical hurdle’ to aggregation).

They argue that within-group variability provides a reflection of climate strength, which is a useful concept in organisational research, a point reinforced by LeBreton and Senter. One of the same metrics used to justify pooling work unit data, the Rwg(j) measure of agreement, is also the most common measure of climate strength. Low Rwg(j) for a particular unit indicates a weak climate (lack of staff agreement on a PSC survey), while high Rwg(j) for a unit indicates a strong climate (high agreement on a PSC survey). It is important to stress that climate level and climate strength are different: units can have a strong negative safety climate, a weak negative safety climate, a strong positive safety climate, and so on across all combinations. Related to the dispersion model, there is also meaningful information about a unit's PSC that can be seen in the shape of the distribution of individual safety climate opinions. Units that do not ‘agree’ on PSC according to standard statistical criteria (low agreement, eg, Rwg. Sampling and procedures Accreditation Canada provided the lead author with all anonymised Can-PSCS data collected between April and October 2011 as part of the Qmentum accreditation process.

The complete data set included data from 13 126 survey respondents working in 119 healthcare organisations. These 119 organisations represent the continuum of care, and the 13 126 direct care providers work in hospitals (28%), nursing homes (32%), ambulatory and community-based health organisations (14%), home care agencies (5%), mental health (7%) and other settings. The majority of hospitals and a large proportion of other healthcare organisations in Canada participate in the Accreditation Canada process, which operates on a 4-year cycle. Details of the survey process have been reported previously.

In the present study, we used data from 442 direct providers working in 24 emergency departments (EDs) across Canada that had ≥10 respondents (reporting by unit was available to organisations that had set up this variable in their online Accreditation Canada portal). We included EDs with a minimum of 10 respondents so we would have a sufficient number to examine within-group agreement —the emphasis of this paper. The number of responders ranged from 10 to 35 among the 24 EDs (mean n=18.4, SD=7.8). EDs are also well-suited to our investigation of the information value of climate strength because they are characterised by time-urgent, unstable workflows that can benefit from clear behavioural expectations that exist in strong climates. Survey instrument The Can-PSCS captures staff perceptions of PSC. The survey contains 19 items that measure six dimensions of PSC: (1) organizational leadership support for safety (four items), (2) incident follow-up (three items), (3) supervisory leadership for safety (two items), (4) unit learning culture (four items), (5) enabling open communication I: judgement-free environment (three items) and (6) enabling open communication II: job repercussions of error (three items). These areas are consistent with robust models of safety climate that have been shown to predict safety outcomes.

Some of the Can-PSCS items are unique and others were adapted from work by Singer et al, Hofmann and Mark and the Agency for Healthcare Research and Quality PSC survey. All items are answered using a five-point Likert-type scale (1=strongly disagree, 2=disagree, 3=neutral, 4=agree and 5=strongly agree) and include a ‘not applicable’ option. The Can-PSCS has been validated for use with direct care providers in a wide range of care settings. Analysis To explore the value of paying greater attention to climate strength in safety climate research, our analyses calculated for each ED unit (1) climate level (mean and median score per unit on each Can-PSCS dimension of safety climate), (2) climate strength (because the six Can-PSCS dimensions are multi-item scales, we calculate agreement as Rwg(j) per unit—see the online supplementary technical appendix—Rwg distributions section for details) and (3) the shape of agreement about safety climate (a frequency chart per unit on each of the six Can-PSCS dimensions).

For the data set as a whole, we also calculated the ICCs (ICC(1) and ICC(2)). These metrics provide an indication of within-group agreement (Rwg(j)), within-group and between-group variability (ICC(1)) and the reliability of group means (ICC(2)). As noted, Rwg(j) is a measure of absolute agreement in the ratings endorsed on a PS survey by the members of a work unit. Values of 0.70 are typically used as cut-offs for determining whether within-group agreement is sufficient to justify aggregation.

Rwg(j) values between 0.51 and 0.70 indicate moderate agreement, and values ≥0.71 suggest strong agreement. In situations with multiple climate ratings for different units or organisations, ICC(1) tends to be interpreted as the extent to which individual ratings can be explained by group membership—like an effect size for unit membership with ≥0.05 indicating a substantial group effect. James argues that ICC(1) is the critical metric for deciding whether to aggregate climate perceptions.

ICC(2) is a reliability measure and answers the question: “How reliable are the group means within a sample?” It ranges from 0 to 1, and values of ≥0.70 are typically interpreted as sufficiently reliable to aggregate unit members’ perceptions. We provide these metrics for each of the six Can-PSC dimensions and use the results as a platform to explore the information value of climate strength and suggest ways that researchers and organisations can better exploit PSC survey data.

For fuller discussion of each of these measures, and alternatives to them, please see the online supplementary technical appendix—section 2. Finally, we generate simple histograms of climate scores on two dimensions for pairs of contrasting EDs that achieved similar mean climate scores (ie, similar levels of climate) but quite different degrees of climate strength. We do so as an illustrative example of how we can look at strength of climate and how consideration of strength of climate can add to knowledge of PSC level in a given setting.

Results summarises, across all ED units, the PSC dimension means and SDs, ICC(1), ICC(2), as well as median and range (lowest to highest) of Rwg(j). Column C shows that median Rwg(j) values approach or exceed 0.70 for all six PSC dimensions. It is important to note that Rwg assesses the extent of consensus/agreement within a single unit (in this case, an ED)—‘a construct by group approach’, so a median Rwg of 0.76 for the organisational leadership support for safety dimension means that half of the EDs in our sample had Rwgs >0.76 and half were.

PSC dimension agreement indices As noted, within-group agreement can also be tested with ICC(1) (amount of variance explained by unit membership) and ICC(2) (reliability of unit means). Unlike Rwg, ICC(1) contrasts within-unit and between-unit variability across an entire sample of units—‘a construct by sample approach’.

Results for ICC(1) and ICC(2) were good to acceptable for four of the six PSC dimensions—organisational leadership support for safety, incident follow-up, supervisory leadership for safety and unit learning culture—but low for judgement-free environment and job repercussions of error (see the online supplementary technical appendix—section 4 for a discussion comparing ICC and Rwg results across the Can-PSC dimensions). Neither Rwg(j) nor ICC tests the statistical significance of within-group agreement. However, statistical criteria have recently been proposed for Rwg and one-way random effects analysis of variance (ANOVA) is typically used to test between-group variance for ICC(1)—readers interested in these results, see the online supplementary technical appendix—section 3. Provides histograms for a few EDs that achieved similar mean climate scores (ie, similar climate level) but quite different degrees of climate strength. The histograms provide an illustrative example of what can be gained by looking at climate strength as a focal variable. The top two histograms show the distribution of scores on the supervisory leadership for safety dimension for two EDs, both with mean ED scores around 3.0. ED 7 (top left) had an Rwg=0.39 while ED 21 (top right) had an Rwg=0.79.

The bottom two histograms show the distribution of scores on the job repercussions of error dimension for two EDs with mean scores around 3.0. ED 7 (bottom left) had an Rwg=0.17 while ED 18 (bottom right) had an Rwg=0.86. Illustrative example of climate strength differences. ED, emergency department. We can also examine the above histograms in the context of other approaches to reporting PSC data that present the proportion of respondents on a unit or in an organisation who ‘agree’ or ‘strongly agree’ with items in a PSC dimension—typically described as the proportion who report ‘positive safety climate’. The percentage of respondents who reported a ‘positive safety climate’ on these two pairs of units were very similar (22.2% and 18.2% for supervisory leadership for safety on units 7 and 21, respectively; 22.2% and 25% for job repercussions of error on units 7 and 18, respectively).

Discussion Taken together, our results provide an illustration of the information value lost if we do not consider climate strength when interpreting PSC data from multiple health care units. The range of inter-rater agreement (Rwg) values across the 24 EDs in our sample was very broad, for example, ranging from 0.17 (indicating no agreement) to 0.86 (indicating strong agreement ) across EDs on the job repercussions of error dimension.

Yet, because the median values of agreement (Rwg calculated with a uniform null) exceed 0.70 for all dimensions except for supervisory leadership for safety (median Rwg=0.64), a common practice is to say that staff respondents agree sufficiently in their climate perceptions to aggregate individual-level data to the group level, that is, to let the unit mean on the dimensions be the focus of analysis and feedback (assuming ICCs also justified aggregation). Another common practice is to simply remove the individual ED units that do not meet criterion for good agreement (Rwg.

PSC interventions can be better designed and tailored to individual healthcare units after considering the more holistic safety climate profile we are advocating—for example, an intervention targeting a weak PSC is likely to look different than an intervention targeting a low PSC. Weak climates might be best addressed by group interventions to develop, articulate and strengthen the unit's safety norms. Low PS climates might be better remediated with strong shows of leadership support for safety, such as increasing supervisor walkarounds, or engaging in high-profile communications and decisions that truly prioritise PS. Our inclusion of safety climate shape as a key piece of a unit's profile provides novel information about how to improve or strengthen safety climate. For example, some units may show evidence of subgroups who perceive quite low versus quite high levels of safety climate. Such units may require an intervention process to understand the natural fault lines of the team and the reason for their different safety experiences—in fact, conflict resolution processes may be required to iron out underlying disagreements over tasks or processes. Focus groups with a neutral, outside facilitator may be necessary to get a frank understanding of the subgroups reporting low safety values.

In climate research, we advocate paying attention to climate strength as an important research variable in its own right. Climate level and strength are both useful aggregate or unit-level variables. For example, strength of climate can be used to predict important research outcomes such as medical errors. So PSC researchers should continue the existing practice of checking agreement of units (Rwg mean, median and range across units), but we argue that the aggregated climate level as well as a metric of strength be considered focal analysis variables. In practical terms, both the mean or median, and the Rwg or SD value, can be unit-level variables of substantive interest in statistical models.

We also advocate noting the shape of unit safety climates, and this can provide a novel typology of unit safety ‘personality’ to consider as a group-level antecedent or consequence in safety research. Researchers can generate a simple histogram or frequency distribution of the safety scale responses of each unit studied and categorise them into ‘types’ (some likely possibilities include a normally distributed shape, extreme bimodal distributions indicating strong fault lines between those perceiving positive and negative culture, and a rectangular shape in which all response options are equally popular, indicating disagreement but no real subgroups). Interesting research questions follow, such as which safety and leadership practices create safety fault lines, as demonstrated in teams with subgroups perceiving very high and very low level safety climates? In terms of the theory of organisational culture, what does it mean when a unit has no clear safety climate, that is, it shows across-the-board safety perceptions (ranging from very negative, through neutral and very positive)? Analytic attention to climate strength sets the scene for PS researchers to provide clarity on the ‘safety climate–safety outcomes’ relationship by looking at whether climate strength moderates the relationship between safety climate level and safety outcomes.

As noted, evidence of this moderating effect of climate strength exists in the broader organisational literature. Focusing our research in these ways on climate strength is likely to help support (or refute) the importance of safety climate as a key variable in the domain of PS. Some of the challenges associated with measuring the relationship between safety climate and outcomes have been identified previously; however, it may be that a focus on the relationship between climate and outcomes that fails to include the role of climate strength as a moderator is susceptible to omitted-variable bias, which occurs when a model leaves out important explanatory variables (eg, climate strength) and, as a result, may overestimate or underestimate the effect of other variables (ie, climate level). Accordingly, we suggest that closer examination of climate strength as a moderator of the climate level–outcomes relationship should be a priority area in PSC research. Limitations and future research This study has some limitations. First, we only examined strength of climate at the unit/department level.

Future research is required to more closely examine PS climate strength and agreement issues at the organisation level (in addition to the unit level). Such work is important given that two key dimensions of PS climate reflect leadership support for PS at both of these levels. Relatedly, the field would benefit from additional research that examines the extent to which PS climate perceptions are explained by unit versus organisation membership as there are only a small number of studies (eg, Schwendimann et al ) in this area. Second, our sample size per ED was n>10. While 10 subjects per group are sufficient for calculating Rwg, Rwgs may be attenuated when only 10 responders are used per unit (particularly when 0.70), sufficient between-group variance (ICC(1)>0.05 or significant between-group ANOVA results) and sufficient group mean reliability (ICC(2)>0.70) before going forward with aggregation. The field would also benefit from other avenues of research on safety climate strength including examination of its antecedents and outcomes and whether these are different for climate level versus climate strength. For instance, do frequently used interventions such as leadership walkarounds mostly affect climate strength, climate level or both?

Relatedly—do units with stronger agreement tend to have higher levels of safety climate? And, as noted above, do units with strong and positive climates have better safety outcomes? More broadly, we hope that this work can also help move our thinking beyond sharedness as a defining aspect of culture and expand the conversation about what constitutes a culture and how culture operates. Conclusion Organisations or units wishing to obtain a holistic picture of their safety climate(s) will have a much more complete picture if they examine both the level and the strength of climate scores, and consider also the shape of units’ climate profiles.

Examining a unit's mean score (ie, level), the SD of the climate score and a simple histogram of the scores can provide this comprehensive picture. Focusing on PSC level and strength can also further knowledge of the extent to which PSC is a key variable in the domain of PS.

Contributors: LG and DGO jointly designed the study, conducted all analyses and drafted and revised the paper. Competing interests: None declared.

Ethics approval: Because the data we report in this paper were provided in anonymised form to the lead author for purposes of secondary analyses, the analyses reported here were exempt from review by the Office of Research Ethics at York University where the lead author is employed. Provenance and peer review: Not commissioned; externally peer reviewed. Download Torrent Discografia Completa Aerosmith.