Assessing the Well-Being at Work of Nurses and Doctors in Hospitals: Protocol for a Scoping Review of Monitoring Instruments

Background: Well-being at work can be defined as “creating an environment to promote a state of contentment which allows an employee to flourish and achieve their full potential for the benefit of themselves and their organisation.” In the health care context, well-being at work of nurses and doctors is important for good patient care. Moreover, it is strongly associated with individual-and organization-level consequences. Relevant literature presents models and concepts of physical, mental, and social well-being. This study uses the 6 elements of the job demands-resources (JD-R) model to interpret well-being at work (job demands, job resources, personal resources, leadership, well-being, and outcomes) as part of a Netherlands Federation of University Medical Hospitals program to find ways to improve and monitor health care professionals’ well-being in Dutch hospitals. Many instruments exist to measure well-being at work in terms of population, setting, and other aspects. An overview of available and eligible instruments assessing and monitoring the well-being of nurses and doctors is currently missing. Objective: We will perform a scoping review aiming to provide an overview of validated instruments assessing and monitoring the well-being of nurses and doctors at work. Methods: We will perform a search of published literature in the following databases: Medline, Embase, and CINAHL.


Introduction
Well-being at work for nurses and doctors employed in hospitals is an important condition for achieving effective, safe, and good patient care [1][2][3]. Moreover, well-being at work is strongly associated with serious consequences at an individual level, such as poor work-life balance [3,4], obesity [3], reduced quality of life of the health care worker [3,5], substance abuse, and suicide [3,6]. At an organizational level it is related to high staff turnover [3,6], absenteeism [3,7], and high health care costs [3].
The Chartered Institute of Personnel and Development defines well-being at work as "creating an environment to promote a state of contentment which allows an employee to flourish and achieve their full potential for the benefit of themselves and their organisation" [8]. Well-being comprises psychological well-being, physical well-being, and social well-being [9]. Also, a variety of published literature presents models and concepts of well-being. Deci et al [10] reported the self-determination theory. The self-determination theory suggests that both employees' performance and their well-being are affected by the type of motivation they have experienced [10]. For medical students, the "coping reserve tank" was illustrated [11]. This is a coping reservoir that can be replenished or drained by various factors, such as stress, mentorship, time demands, and support [11]. Potential outcomes were described as resilience versus burnout [11]. Schaufeli [12] developed the job demands-resources model (JD-R model) facilitating communication about "work and well-being." In essence, the JD-R model integrates 2 processes: the stress process, which is sparked by excessive job demands and lack of resources, and a motivational process, which is triggered by abundant job resources and may lead to positive outcomes, such as organizational commitment, intention to stay, and work performance [12]. Thus, different components, including resources (eg, support, development opportunities, team atmosphere), demands (eg, stress, workload, conflicts), and personal resources (eg, leadership, intrinsic motivation, resilience), contribute to positive well-being (eg, job satisfaction) or negative well-being (eg, burnout). Abundant resources and reasonable demands will result in positive outcomes (eg, performance, commitment); the reverse will lead to negative outcomes (sickness, absenteeism) [12].
Measuring well-being at work among doctors and nurses is not easy, since this multidimensional concept encompasses diverse elements [8,13]. Instruments to measure well-being at work vary in specific professions [14] and specific settings [15] or include only one or two aspects of well-being at work [12,16]. This study is part of a program of the Netherlands Federation of University Medical Hospitals (NFU) about finding ways to improve and monitor health care professionals' well-being in Dutch hospitals. Strikingly, there is often a substantial gap and mismatch between employer perceptions of well-being and well-being interventions [8]. It is important to develop and implement suitable interventions adapted to the needs of nurses and doctors and their health and other problems [8,17]. Well-being screening should be timely to gain insight into these needs and problems. Moreover, measurement to determine the effectiveness of well-being interventions is mandatory [8,17].
To summarize, when hospitals assess and monitor the well-being of nurses and doctors with validated instruments, they can, in a timely manner, design and start tailored interventions to prevent negative well-being in the workplace, thereby contributing to sustainable employability and quality of care [18,19]. An overview of available and eligible instruments assessing and monitoring the well-being of nurses and doctors is currently missing. Scoping reviews are helpful to explore broad, complex, and heterogeneous literature [20]. For this study, a scoping review is planned to obtain an overview of the field of well-being instruments and identify its breadth.

Aim
This scoping review aims to identify instruments monitoring the well-being at work of nurses and doctors working in hospitals. The Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) [21] and the 6-stage framework of Arksey and O'Malley [22] for conducting scoping reviews will be used.

Information Sources
A priori, possible previous reviews of this topic were checked. Published articles will be searched in the following electronic databases: Medline, Embase and CINAHL. To avoid including outdated literature, we will include only articles published after 2011 [23]. To identify instruments that are applied in present clinical practice, preference is given to more-frequent, lower-volume searches to fit the exponentially changing field of health care [24,25]. Gray literature will not be used as an information source.

Search Strategy
The search strategy was created by AB with assistance by an information specialist and the research team. Several terms derived from the research aim were identified to develop search strings to find relevant literature. Keywords and Medical Subject Heading (MeSH) terms related to the domain (nurses and doctors working in hospitals), the determinants (instruments for monitoring), and the outcome (well-being at work) will be used. The first draft of the search string was based on 5 relevant, previously published "golden bullets," sample articles that had to be found in the data set. Afterwards, a test data set was screened by the first author to optimize the search string. After this screening, the data set turned out to be too large (more than 17,000 studies), so to ensure the feasibility of the study it was decided to make the search more specific. For the search strategy see Tables 1, 2, and 3.

Results
Query a ID 1329 S17 AND S10 AND S4 AND S3 S1 229 S17 AND S10 AND S4 AND S3 S2 46,447 S9 OR S8 OR S7 OR S6 OR S5 S3 421,434 S25 OR S24 OR S23 OR S22 S4 145,799 TI(satisfaction or well-being or fulfilment or burnout or ((psychological or mental) N1 health) or thriving or environment or ethic*) S5 36,566 AB(satisfaction or well-being or fulfilment or burnout or ((psychological or mental) N1 health) or thriving or environment or ethic*) S6 44,106 (MH "Mental Health") S7 35,259 (MH "Job Satisfaction") OR (MH "Personal Satisfaction") S8 12,506 (MH "Burnout, Professional") S9 502,496 S15 OR S14 OR S13 OR S12 OR S11 S10 80,499 TI(valid* or (cronbach* N3 (alpha or alphas)) or interrater or inter-rater or intrarater or intra-rater or intertester or inter-tester or intratester or intra-tester or interobserver or inter-observer or intraobserver or intraobserver or interexaminer or inter-examiner or intraexaminer or intra-examiner or interindividual or inter-individual or intraindividual or intra-individual or kappa or kappa?s or kappas or ((replicab* or repeated) and (measure or measures or findings or result or results or test or tests)) or concordance or (intraclass and correlation*) or (uncertainty and (measurement or measuring)) or "standard error of measurement" or sensitiv*) S11 417,988 AB(valid* or (cronbach* N3 (alpha or alphas)) or interrater or inter-rater or intrarater or intra-rater or intertester or inter-tester or intratester or intra-tester or interobserver or inter-observer or intraobserver or intraobserver or interexaminer or inter-examiner or intraexaminer or intra-examiner or interindividual or inter-individual or intraindividual or intra-individual or kappa or kappa?s or kappas or ((replicab* or repeated) and (measure or measures or findings or result or results or test or tests)) or concordance or (intraclass and correlation*) or (uncertainty and (measurement or measuring)) or "standard error of measurement" or sensitiv*) S12 17,505 (MH "Kappa Statistic") S13 67,821 (MH "Reproducibility of Results") S14 27,493 (MH "Interrater Reliability") S15 82,033 S25 OR S24 OR S23 OR S22 OR S21 S16 753,761 S20 OR S19 OR S18 S17 12,483 TI(clinimetr* or clinometr* or psychometr* or survey? or score or scale or subscale or (measurement N3 instrument) or subscale* or item-discriminant or interscale correlation* or "ceiling effect" or "floor effect" or "Item response model" or Rasch or "Differential item functioning" or "item bank" or (item N3 (correlation* or selection* or reduction* or bank))) S18 694,621 AB(clinimetr* or clinometr* or psychometr* or survey? or score or scale or subscale or (measurement N3 instrument) or subscale* or item-discriminant or interscale correlation* or "ceiling effect" or "floor effect" or "Item response model" or Rasch or "Differential item functioning" or "item bank" or (item N3 (correlation* or selection* or reduction* or bank))) S19 32,107 (MH "Psychometrics") OR (MH "Measurement Issues and Assessments") S20 556,739 AB("Healthcare professionals" or Caregivers or "Healthcare providers" or Practitioners or Doctor or nurse? or physician? or resident? or "healthcare worker" or "health staff") S21 296,467 TI("Healthcare professionals" or Caregivers or "Healthcare providers" or Practitioners or Doctor or nurse? or physician? or resident? or "healthcare worker" or "health staff")  Results Query a ID 1329 S17 AND S10 AND S4 AND S3 S1 229 S17 AND S10 AND S4 AND S3 S2 502,496 S15 OR S14 OR S13 OR S12 OR S11 S10 80,499 TI(valid* or (cronbach* N3 (alpha or alphas)) or interrater or inter-rater or intrarater or intra-rater or intertester or inter-tester or intratester or intra-tester or interobserver or inter-observer or intraobserver or intraobserver or interexaminer or inter-examiner or intraexaminer or intra-examiner or interindividual or inter-individual or intraindividual or intra-individual or kappa or kappa?s or kappas or ((replicab* or repeated) and (measure or measures or findings or result or results or test or tests)) or concordance or (intraclass and correlation*) or (uncertainty and (measurement or measuring)) or "standard error of measurement" or sensitiv*) S11 417,988 AB(valid* or (cronbach* N3 (alpha or alphas)) or interrater or inter-rater or intrarater or intra-rater or intertester or inter-tester or intratester or intra-tester or interobserver or inter-observer or intraobserver or intraobserver or interexaminer or inter-examiner or intraexaminer or intra-examiner or interindividual or inter-individual or intraindividual or intra-individual or kappa or kappa?s or kappas or ((replicab* or repeated) and (measure or measures or findings or result or results or test or tests)) or concordance or (intraclass and correlation*) or (uncertainty and (measurement or measuring)) or "standard error of measurement" or sensitiv*) S12 17,505 (MH "Kappa Statistic") S13 67,821 (MH "Reproducibility of Results") S14 27,493 (MH "Interrater Reliability") S15 82,033 S25 OR S24 OR S23 OR S22 OR S21 S16 753,761 S20 OR S19 OR S18 S17 12,483 TI(clinimetr* or clinometr* or psychometr* or survey? or score or scale or subscale or (measurement N3 instrument) or subscale* or item-discriminant or interscale correlation* or "ceiling effect" or "floor effect" or "Item response model" or Rasch or "Differential item functioning" or "item bank" or (item N3 (correlation* or selection* or reduction* or bank))) S18 694,621 AB(clinimetr* or clinometr* or psychometr* or survey? or score or scale or subscale or (measurement N3 instrument) or subscale* or item-discriminant or interscale correlation* or "ceiling effect" or "floor effect" or "Item response model" or Rasch or "Differential item functioning" or "item bank" or (item N3 (correlation* or selection* or reduction* or bank))) S19 32,107 (MH "Psychometrics") OR (MH "Measurement Issues and Assessments") S20 556,739 AB("Healthcare professionals" or Caregivers or "Healthcare providers" or Practitioners or Doctor or nurse? or physician? or resident? or "healthcare worker" or "health staff") S21 296,467 TI("Healthcare professionals" or Caregivers or "Healthcare providers" or Practitioners or Doctor or nurse? or physician? or resident? or "healthcare worker" or "health staff")

Inclusion Criteria
Studies will be eligible if they (1) assess well-being of nurses and doctors working in hospitals; (2) describe an evaluation of an instrument or review an instrument; (3) measure well-being at work or aspects of well-being at work according the elements of the JD-R model and (4) were published in English in or after 2011.

Exclusion Criteria
Studies will be excluded if they (1) describe only the development of the instrument but not its evaluation or (2) have a sample that consists only of students, without employees.

Selection Process
Study screening and selection will be conducted independently by AB, KvdB, and NA using Rayyan. After the removal of duplicate records identified by the search strategy, articles will be screened on titles and abstracts for the inclusion and exclusion criteria. To ensure consistent screening, the first 100 articles will be pilot-screened by all 3 researchers until consensus is reached. After validation, the hits will be allocated to 1 of the 3 researchers. Eligible articles will then be assessed on their full text. To ensure consistent full-text screening, the first 18 articles will be pilot-screened by all 3 researchers until consensus is reached. After validation, the full texts will be subdivided among the 3 researchers. Justification for study inclusion and exclusion will be reported. Inclusion reasons and exclusion reasons will be marked with a label. Uncertainty in selection and the data extraction process will be resolved by consensus [12]. The JD-R model will be used to assess if the reported instruments appropriately measured well-being or aspects of well-being at work (job demands, job resources, engaged leadership, personal resources, employees' well-being, outcomes) [12]. The energy compass of the JD-R model includes all aspects of well-being at work and is applicable to different employees in different settings [12]. Included instruments will be categorized according to the 6 domains of the JD-R model. The domain "job demands" contains the categories "qualitative job demands," "quantitative job demands," and "organizational demands." The domain "job resources" contains the categories "social resources," "work resources," "organizational resources," and "developmental resources." The domain "engaged leadership" contains the categories "inspiring," "strengthening," and "connecting." The domain "personal resources" contains the categories "resilience," "self-efficacy," "optimism," flexibility," "setting one's own limits," "productivity," "goal direction," and "self-development." The domain "well-being" contains the positive and negative categories "burnout," "work engagement," "psychological distress," "boredom," "sleep problems," and "job satisfaction." The domain "outcomes" contains the categories "commitment," "employability," and "performance." Study search and selection will be illustrated using the PRISMA flow diagram.

Data Collection Process
Data extraction and full-text screening will be conducted at the same time. AB, KvdB, and NA will independently use the standardized data extraction form, which will be developed a priori in Excel (Microsoft Corp). The elements that will be charted for each article are (1) study characteristics, including year, authors, country, study ID, and study aim (validation study, measuring variables, both, other); (2) sample characteristics, including type of health care professionals (nurses, doctors, other), setting (hospital, partly hospital), and sample size; (3) measurement instrument, including the name of the instrument, main construct measured, subconstructs measured, job demands, job resources, leadership, personal resources, well-being, and outcomes; and (4) psychometric properties, including validity, reliability, responsiveness, and quality references. The aim of this study is not to evaluate psychometric properties but include only the evaluated instruments. Therefore, information about psychometric properties (whether validity, reliability, and responsiveness are tested) will be extracted. When no psychometric properties are reported, the references mentioned for evaluation will be extracted. Table 4 shows further details.
All instruments meeting the inclusion criteria will be extracted from studies. If more than one instrument has been described, instrument details will be extracted separately for each instrument. The data extraction form will be tested by AB, KvdB, and NA on 18 studies to ensure adequacy of the extraction. Disagreements will be discussed, and the form will be refined, if necessary, after the pilot phase with the 18 studies.

Measurement instrument
Full name of the instrument, including any abbreviations (eg, Professional Quality of Life Scale, version 5; ProQol-5).

Name of instrument
Main construct intended to be measured (eg, professional quality of life) Main construct measured Subthemes intended to be measured (eg, compassion satisfaction, secondary traumatic stress, burnout)

Subconstructs measured
Picklist JD-R a demands • Job demands: the outcome fits with "job demands" according to the JD-R model.

Risk of Bias
Evaluating evidence quality is not applicable for scoping reviews [22,27]. AB, KvdB, and NA are junior researchers with relatively little experience in conducting scoping reviews on the topic of the well-being of nurses and doctors, but they are supervised by senior researchers with ample experience. The variety of backgrounds, opinions, experiences, and perspectives within the interdisciplinary research team supports self-reflectivity about the subjective values, biases, and inclinations of the researchers. In addition to the medical doctors, other health care professional types are also represented within the research team and have experience with the well-being topic. The first author is a nurse and KvdB has a physiotherapy background. Likewise, LH specializes in the methodology of reviews.

Data Analysis
This scoping review will determine the size and nature of the evidence base for instruments evaluating well-being at work. The analysis will be descriptive and findings will be assimilated, synthesized, and described. The plan is to summarize validated well-being instruments in tables and figures according to the elements of the JD-R model. Additionally, we will report how often instruments have been extracted, which aspects of well-being are measured, and which types of health care professional are targeted. Synthesizing the data, a distinction will be made between comprehensive instruments and common instruments. The more JD-R domains an instrument encompasses, the more comprehensive (measuring entire well-being) the instrument will be considered to be. Most common instruments are instruments occurring frequently. Results will help readers interpret and choose fitting instruments.

Ethical Considerations
This study does not require ethical approval since it is a literature review.

Results
Studies were searched between September 2021 and December 2021. Studies were screened between December 2021 and May 2022. A total of 739 studies were included. Thereafter, data extraction and full text screening were conducted at the same time. The protocol was submitted for publication in October 2022. Before submission, we carried out our search and started screening and extracting data from relevant articles but had no insight into the overall data and had not yet performed data analysis.

Discussion
This scoping review gives an overview of instruments that have been developed and validated for monitoring the well-being of nurses and doctors at work.
Published research shows a great variety in concepts and measures of well-being at work in terms of target population (professions), settings, and aspects of well-being at work. However, a critical, overarching analysis of these concepts and measures is missing in the scientific literature. There is often a substantial gap and mismatch between employer perceptions of well-being and well-being interventions [8]. It is important to develop and implement suitable interventions adapted to the needs of nurses and doctors and their health or other problems [8,17]. Timely screening of well-being is necessary to gain insight into these needs and problems. Moreover, it is necessary to determine the effectiveness of well-being interventions [8,17].
Our study has some limitations that need to be considered. First, the articles will be divided among 3 researchers because of the amount of data. Consequently, data screening and extraction will be performed once. In order to avoid bias, pilot sets will be screened until consensus on eligibility criteria and JD-R classification is reached. Additionally, an audit trail is used to describe and link thoughts, doubts, and methodological choices.