While performance management (PM) is pervasive across contemporary workplaces, extant research into how performance management affects workers is often indirect or scattered across disciplinary silos. This paper reviews and synthesizes this research, identifies key gaps and explores ‘recognition theory’ as a nascent framework that can further develop this important body of knowledge. The paper develops in three main stages. The first stage reviews ‘mainstream’ human resource management (HRM) research. While this research analyses workers’ reactions to performance management in some depth, its focus on serving organizational goals marginalizes extra‐organizational impacts. The second stage reviews more critical HRM research, which interprets performance management as a disciplinary, coercive or inequitable management device. While this literature adds an important focus on organizational power, there is scope to analyse further how PM affects workers’ well‐being. To develop this strand of PM research, the third stage turns to the emerging field of recognition theory independently developed by Axel Honneth and Christophe Dejours. The authors focus especially on recognition theory's exploration of how (in)adequate acknowledgement of workers’ contributions can significantly affect their well‐being at the level of self‐conception. Although recognition theory is inherently critical, the paper argues that it can advance both mainstream and critical performance management research, and also inform broader inquiry into recognition and identity at work.

Introduction: One-third of the general public will not accept Coronavirus disease 2019 (COVID-19) vaccination but factors influencing vaccine acceptance among health care personnel (HCP) are not known. We investigated barriers and facilitators to vaccine acceptance within 3 months of regulatory approval (primary outcome) among adult employees and students at a tertiary-care, academic medical center.Methods: We used a cross-sectional survey design with multivariable logistic regression. Covariates included age, gender, educational attainment, self-reported health status, concern about COVID-19, direct patient interaction, and prior influenza immunization.Results: Of 18,250 eligible persons, 3,347 participated. Two in 5 (40.5%) HCP intend to delay (n = 1020; 30.6%) or forgo (n = 331; 9.9%) vaccination. Male sex (adjusted OR [aOR], 2.43; 95% confidence interval [CI], 2.00-2.95; P < .001), prior influenza vaccination (aOR, 2.35; 95% CI, 1.75-3.18; P < .001), increased concern about COVID-19 (aOR, 2.40; 95% CI, 2.07-2.79; P < .001), and postgraduate education (aOR, 1.41; 95% CI, 1.21-1.65; P < .001) -but not age, direct patient interaction, or self-reported overall health -were associated with vaccine acceptance in multivariable analysis. Barriers to vaccination included concerns about long-term side effects (n = 1197, 57.1%), safety (n = 1152, 55.0%), efficacy (n = 777, 37.1%), risk-to-benefit ratio (n = 650, 31.0%), and cost (n = 255, 12.2%).Subgroup analysis of Black respondents indicates greater hesitancy to accept vaccination (only 24.8% within 3 months; aOR 0.13; 95% CI, 0.08-0.21; P < .001).Conclusions: Many HCP intend to delay or refuse COVID-19 vaccination. Policymakers should impartially address concerns about safety, efficacy, side effects, risk-to-benefit ratio, and cost. Further research with minority subgroups is urgently needed.

Background The COVID-19 pandemic has highlighted the inability of health systems to leverage existing system infrastructure in order to rapidly develop and apply broad analytical tools that could inform state- and national-level policymaking, as well as patient care delivery in hospital settings. The COVID-19 pandemic has also led to highlighted systemic disparities in health outcomes and access to care based on race or ethnicity, gender, income-level, and urban-rural divide. Although the United States seems to be recovering from the COVID-19 pandemic owing to widespread vaccination efforts and increased public awareness, there is an urgent need to address the aforementioned challenges. Objective This study aims to inform the feasibility of leveraging broad, statewide datasets for population health–driven decision-making by developing robust analytical models that predict COVID-19–related health care resource utilization across patients served by Indiana’s statewide Health Information Exchange. Methods We leveraged comprehensive datasets obtained from the Indiana Network for Patient Care to train decision forest-based models that can predict patient-level need of health care resource utilization. To assess these models for potential biases, we tested model performance against subpopulations stratified by age, race or ethnicity, gender, and residence (urban vs rural). Results For model development, we identified a cohort of 96,026 patients from across 957 zip codes in Indiana, United States. We trained the decision models that predicted health care resource utilization by using approximately 100 of the most impactful features from a total of 1172 features created. Each model and stratified subpopulation under test reported precision scores >70%, accuracy and area under the receiver operating curve scores >80%, and sensitivity scores approximately >90%. We noted statistically significant variations in model performance across stratified subpopulations identified by age, race or ethnicity, gender, and residence (urban vs rural). Conclusions This study presents the possibility of developing decision models capable of predicting patient-level health care resource utilization across a broad, statewide region with considerable predictive performance. However, our models present statistically significant variations in performance across stratified subpopulations of interest. Further efforts are necessary to identify root causes of these biases and to rectify them.

BACKGROUND The COVID-19 pandemic has highlighted the inability of health systems to leverage existing system infrastructure to rapidly develop and apply broad analytical tools that could inform state and national-level policymaking as well as patient care delivery at hospital settings. COVID-19 has also led to highlighted systemic disparities in health outcomes and access to care based on race/ethnicity, gender, income-level and urban-rural divide. While the US seems to be recovering from the COVID-19 pandemic due to widespread vaccination efforts and increased public awareness, there is an urgent need to address the aforementioned challenges. OBJECTIVE Inform the feasibility of leveraging broad, statewide datasets for population-health driven decision making by developing robust analytical models that predict COVID-19 related healthcare resource utilization across patients served by Indiana’s statewide Health Information Exchange (HIE). METHODS We leveraged comprehensive datasets obtained from the Indiana Network for Patient Care (INPC) to train decision forest-based models that predicted patient-level need of healthcare resource utilization. To assess models for potential biases, we tested model performance against sub-populations stratified by age, race/ethnicity, gender, and residence (urban vs. rural). RESULTS We identified a cohort of 96,190 patients from 957 zip codes spread across the state of Indiana. We trained decision models that predicted healthcare resource utilization using the most impactful features (~100) out of a total of 1172 features created. Each model and stratified sub-population under test reported precision scores > 70%, accuracy and AUC ROC scores > 80%, and sensitivity scores ~>90%. We noted statistically significant variations in model performance across stratified sub-populations identified by age, race/ethnicity, gender, and residence (urban vs. rural). CONCLUSIONS This study presents the possibility of developing decision models capable of predicting patient-level healthcare resource utilization across a broad statewide region with considerable predictive performance. However, our models present statistically significant variations in performance across stratified sub-populations of interest. Further efforts are necessary to identify root causes of these biases and to rectify them. CLINICALTRIAL NA