Dataset Information

The 'goodness-of-fit' of fit models: creating a multidimensional survey for person-organisation and person-group fit in health care.

ABSTRACT: BACKGROUND:Person-environment fit, which examines the individual's perceptions of if, and in what way, he or she is compatible with aspects of the work context, offers a promising conceptual model for understanding employees and their interactions in health care environments. There are numerous potential ways an individual feels they "fit" with their environment. The construct was first noted almost thirty years ago, yet still remains elusive. Feelings of fit with one's environment are typically measured by surveys, but current surveys encompass only a subset of the different components of fit, which may limit the conclusions drawn. Further, these surveys have rarely been conducted in a focused way in health care settings. METHOD:This article describes the development of a multidimensional survey tool to measure fit in relation to the person's work group (termed person-group (P-G) fit) and their organisation (person-organisation (P-O) fit). The participants were mental health care employees, volunteers, and university interns (n =?213 for P-O fit; n =?194 for P-G fit). Confirmatory Factor Analyses (CFAs) were conducted using LISREL. RESULTS:Valid and reliable sub-scales were found. CONCLUSION:This advanced multidimensional survey tool can be used to measure P-O and P-G fit, and illuminates new information about the theoretical structure of the fit construct.

SUBMITTER: Herkes J

PROVIDER: S-EPMC7275356 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The 'goodness-of-fit' of fit models: creating a multidimensional survey for person-organisation and person-group fit in health care.

Herkes J J Ellis L A LA Churruca K K Braithwaite J J

BMC medical research methodology 20200605 1

<h4>Background</h4>Person-environment fit, which examines the individual's perceptions of if, and in what way, he or she is compatible with aspects of the work context, offers a promising conceptual model for understanding employees and their interactions in health care environments. There are numerous potential ways an individual feels they "fit" with their environment. The construct was first noted almost thirty years ago, yet still remains elusive. Feelings of fit with one's environment are t ...[more]

PMID: 32503435

Similar Datasets

Project description:ObjectivesPeople interact with their work environment through being, to a greater or lesser extent, compatible with aspects of their setting. This interaction between person and environment is particularly relevant in healthcare settings where compatibility affects not only the healthcare professionals, but also potentially the patient. One way to examine this association is to investigate person-organisation (P-O) fit and person-group (P-G) fit. This systematic review aimed to identify and synthesise knowledge on both P-O fit and P-G fit in healthcare to determine their association with staff outcomes. It was hypothesised that there would be a positive relationship between fit and staff outcomes, such that the experience of compatibility and 'fitting in' would be associated with better staff outcomes.DesignA systematic review was conducted based on an extensive search strategy guided by Preferred Reporting Items for Systematic review and Meta-Analyses to identify relevant literature.Data sourcesCINAHL Complete, EMBASE, Ovid MEDLINE, PsycINFO and Scopus.Eligibility criteriaArticles were included if they were empirical studies, published in peer-reviewed journals in English language, set in a healthcare context and addressed the association that staff outcomes have with P-O and/or P-G fit.Data extraction and synthesisIncluded texts were examined for study characteristics, fit constructs examined and types of staff outcomes assessed. The Quality Assessment Tool was used to assess risk of bias.ResultsTwenty-eight articles were included in the review. Of these, 96.4% (27/28) reported a significant, positive association between perception of fit and staff outcomes in healthcare contexts, such that a sense of compatibility had various positive implications for staff, including job satisfaction and retention.ConclusionAlthough the results, as with all systematic reviews, are prone to bias and definitional ambiguity, they are still informative. Generally, the evidence suggests an association between employees' perceived compatibility with the workplace or organisation and a variety of staff outcomes in healthcare settings.

Project description:OBJECTIVES:Organisational and workplace cultures are fundamental determinants of health systems performance; through better understanding of the dimensions of culture there is the potential to influence them, and subsequently improve safety and quality of care, as well as the experiences of both patients and staff. One promising conceptual framework for studying culture in healthcare is person-environment (P-E) fit. Comprising person-organisational (P-O) and person-group (P-G) components, P-E fit is defined as the extent to which individuals are compatible with their work environment. The aim of this study was to examine the associations of P-O and P-G fit with staff outcomes in mental healthcare. SETTING AND PARTICIPANTS:Participants (n=213) were staff and volunteers at 31 primary mental health facilities across six states of Australia. PRIMARY AND SECONDARY OUTCOME MEASURES:Staff outcomes, comprising burnout (depersonalisation and emotional exhaustion), job satisfaction and work stress. DESIGN:A multidimensional survey tool was used to measure P-O and P-G fit, and staff outcomes. Multiple regression analyses were used to test the associations between fit and outcome measures. RESULTS:The regression analyses indicated that, based on a Bonferroni adjusted alpha value of α=00417, P-O fit accounted for 36.6% of the variability in satisfaction (F=8.951, p≤0.001); 27.7% in emotional exhaustion (F=6.766, p≤0.001); 32.8% in depersonalisation (F=8.646, p≤0.001); and 23.5% in work stress (F=5.439, p≤0.001). The P-G fit results were less conclusive, with P-G fit accounting for 15.8% of the variability in satisfaction (F=4.184, p≤0.001); 10.0% in emotional exhaustion (F=2.488, p=0.014); 28.6% in depersonalisation (F=8.945, p≤0.001); and 10.4% in work stress (F=2.590, p=0.032). There was no statistically significant increase in the variability accounted for when the interaction term of P-O and P-G fit was added to the regression. CONCLUSIONS:The findings highlight that staff's perception of their workplace and organisational culture can have implications for staff well-being.

Project description:Occupancy modeling is important for exploring species distribution patterns and for conservation monitoring. Within this framework, explicit attention is given to species detection probabilities estimated from replicate surveys to sample units. A central assumption is that replicate surveys are independent Bernoulli trials, but this assumption becomes untenable when ecologists serially deploy remote cameras and acoustic recording devices over days and weeks to survey rare and elusive animals. Proposed solutions involve modifying the detection-level component of the model (e.g., first-order Markov covariate). Evaluating whether a model sufficiently accounts for correlation is imperative, but clear guidance for practitioners is lacking. Currently, an omnibus goodness-of-fit test using a chi-square discrepancy measure on unique detection histories is available for occupancy models (MacKenzie and Bailey, Journal of Agricultural, Biological, and Environmental Statistics, 9, 2004, 300; hereafter, MacKenzie-Bailey test). We propose a join count summary measure adapted from spatial statistics to directly assess correlation after fitting a model. We motivate our work with a dataset of multinight bat call recordings from a pilot study for the North American Bat Monitoring Program. We found in simulations that our join count test was more reliable than the MacKenzie-Bailey test for detecting inadequacy of a model that assumed independence, particularly when serial correlation was low to moderate. A model that included a Markov-structured detection-level covariate produced unbiased occupancy estimates except in the presence of strong serial correlation and a revisit design consisting only of temporal replicates. When applied to two common bat species, our approach illustrates that sophisticated models do not guarantee adequate fit to real data, underscoring the importance of model assessment. Our join count test provides a widely applicable goodness-of-fit test and specifically evaluates occupancy model lack of fit related to correlation among detections within a sample unit. Our diagnostic tool is available for practitioners that serially deploy survey equipment as a way to achieve cost savings.

Dataset Information

The 'goodness-of-fit' of fit models: creating a multidimensional survey for person-organisation and person-group fit in health care.

Publications

The 'goodness-of-fit' of fit models: creating a multidimensional survey for person-organisation and person-group fit in health care.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets