A systematic review of interventions aiming to improve newly-qualified doctors’ wellbeing in the United Kingdom
Bibliographic record
Abstract
BACKGROUND: Newly-qualified doctors in the United Kingdom experience a great deal of stress and have poor wellbeing when compared to more senior counterparts. A number of interventions have been put in place to boost healthcare professionals' wellbeing, but little is known about interventions aimed to improve the wellbeing of newly-qualified doctors in the United Kingdom. This study aims to systematically review current evidence of interventions which improved the wellbeing of newly-qualified junior doctors in the United Kingdom. METHODS: Five key electronic databases were searched. Subsequently, reference scanning and citation search was performed. Studies were included if they were conducted from the commencement of the Foundation Programme in 2004, until 2019. In addition, studies had to be performed on junior doctors: working in the United Kingdom and within their first five years post-qualification and have a quantitative outcome. Studies which did not meet these criteria were excluded. Quality was assessed using the modified Newcastle-Ottawa scale. Bias was not formally assessed using a standardised tool. RESULTS: Seven papers met the inclusion criteria and identified three main types of interventions: mentorship, mindfulness and clinical preparation interventions. The majority of included studies reported a positive result from the performed intervention, suggesting these to be beneficial in improving junior doctor wellbeing, and thereby reducing anxiety and stress levels. However, most of the studies used small sample sizes. CONCLUSIONS: This review reveals that there is dearth of evidence on the effectiveness of intervention to improve the wellbeing of newly-qualified doctors in the United Kingdom. Most of the identified interventions focused on relieving stress and anxiety inherent within newly-qualified doctors' training programmes. However, wellbeing interventions need to take into cognisance all the factors which impact on wellbeing, particularly job-related factors. We recommend that future researchers implement large-scale holistic interventions using appropriate research methods. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42019127341.
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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 | 0.013 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".