Measuring Self-Reported Well-Being of Physicians Using the Well-Being Thermometer: Cohort Study
Bibliographic record
Abstract
Background: Advancements in medical science have focused largely on patient care, often overlooking the well-being of health care professionals (HCPs). This oversight has consequences; not only are HCPs prone to mental and physical health challenges, but the quality of patient care may also endure as a result. Such concerns are also exacerbated by unprecedented crises like the COVID-19 pandemic. Compared to other sectors, HCPs report high incidence of stress, depression, and suicide, among other challenging factors that have a significant negative impact on their well-being. Objective: Given these substantial concerns, the development of a tool specifically designed to be used in clinical settings to measure the well-being of HCPs is essential. Methods: A United Kingdom-based cross-sectional pilot study was carried out to measure self-reported well-being in a cohort of 148 physicians, using the newly developed well-being thermometer. The aim of the tool is to allow respondents to develop an individual sense of "well-being intelligence" thus supporting HCPs to have better insight and control over their well-being and allow insights into how to manage it. The tool consists of 5 well-being domains-health, thoughts, emotions, spiritual, and social. Each domain can be measured individually or combined to produce an overall well-being score. Results: The tool demonstrated good internal consistency; the Cronbach α in this study was 0.84 for the total scale. Conclusions: Results from this cohort demonstrated that the well-being thermometer can be used to gather intelligence of staff well-being. This is a promising new tool that will assist HCPs to recognize their own well-being needs and allow health care organizations to facilitate change in policies and practices to reflect a better understanding of staff well-being.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".