![]() ![]() Prior to calibrating each IRT model, we evaluated traditional item- and scale-level descriptive statistics IRT model assumptions and model-, item-, and person-level fit (Supplemental Appendix 1). The GRM model assumes that physicians’ item responses are a function of one primary, continuous underlying construct (unidimensionality) item responses are independent after controlling for the underlying burnout construct (local independence) and the probability of endorsing successively higher item response categories increases as physicians’ underlying burnout symptom levels increase (monotonicity). Item discrimination parameters indicate the degree to which an item differentiates between physicians who have high versus low burnout symptom levels (with higher values yielding more scale precision). IRT scores, item threshold parameters, and item symptom severity values are on z-score metric (0 = mean, SD = 1). The mean of item threshold estimates from each calibrated IRT model describe the burnout symptom severity (item difficulty) represented by each item. Item threshold parameters represent the IRT score at which a randomly selected physician among those with that score would have a cumulative probability of endorsing a particular response category or higher of 0.50. For each MBI subscale item, the GRM predicted the cumulative probability of responding in a particular item response category or higher (e.g., “once a week” to “every day”) as a function of physicians’ underlying (latent) burnout symptom levels (i.e., an IRT score ( θ)), item threshold parameters ( b x j), and an item discrimination parameter ( a j). We calibrated IRT models for each MBI subscale using unidimensional, graded response models (GRM). As a secondary aim, we evaluated the precision bandwidth of each MBI subscale relative to where US physicians’ scores are distributed on each metric. We produced a crosswalk mapping raw (total) MBI subscale scores to scaled (IRT-based) scores and associated response profiles. Our primary aim was to create response profiles describing the probability of burnout symptoms across standardized MBI subscale scores in US physicians. In this study, we leveraged the content-referenced and norm-referenced score interpretation of IRT-calibrated (estimated) models to better understand the meaning of MBI subscale scores in a national US physician sample. ![]() However, no studies have applied IRT methods to evaluate the MBI in a national sample of US physicians. IRT analyses are routinely used in health outcome measurement and are part of the NIH Patient Reported Outcome Measurement Information System (PROMIS) scientific standards for health outcome measurement development and validation. Using IRT to estimate physicians’ probability of endorsing MBI subscale items across different burnout symptom severity levels, scores can be interpreted based on how likely a physician is to endorse a particular item (e.g., “I feel burned out from my work”) at a particular frequency (e.g., “once a week” or more) and relative to the mean score of the sample (i.e., content-referenced and norm-referenced scoring, respectively). The use of item response theory (IRT) measurement methods can facilitate an enhanced understanding of subscale scores over traditional methods. Traditional measurement methods do not permit users to directly compare subscale scores with the content of items to interpret their meaning. One contributor to the observed inconsistencies in defining dichotomous burnout outcomes on the MBI may be the lack of clarity regarding the meaning of subscale scores. While the MBI is the most widely used physician burnout outcome assessment, a recent systematic review found a lack of consistency in cut-points used to define dichotomous burnout outcomes on each continuous MBI subscale, contributing to a marked heterogeneity in reported burnout prevalences across studies. Current US health policy discussions surrounding the physician burnout crisis have largely been informed by prevalence studies employing the Maslach Burnout Inventory-Human Services Survey for Medical Personnel (MBI). ![]()
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