Returning to clinical work and doctors’ personal, social and organisational needs: a systematic review
Bibliographic record
Abstract
OBJECTIVE: This systematic review aims to synthesise existing evidence on doctors' personal, social and organisational needs when returning to clinical work after an absence. DESIGN: Systematic review using Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. DATA SOURCES: AMED, BNI, CINAHL, EMBASE, EMCARE, HMIC, Medline, PsycINFO and PubMed were searched up to 4 June 2020. Non-database searches included references and citations of identified articles and pages 1-10 of Google and Google Scholar. ELIGIBILITY CRITERIA: Included studies presented quantitative or qualitative data collected from doctors returning to work, with findings relating to personal, social or organisational needs. DATA EXTRACTION AND SYNTHESIS: Data were extracted using a piloted template. Risk of bias assessment used the Medical Education Research Study Quality Instrument or Critical Appraisal Skills Programme Qualitative Checklist. Data were not suitable for meta-analyses and underwent narrative synthesis due to varied study designs and mixed methods. RESULTS: Twenty-four included studies (14 quantitative, 10 qualitative) presented data from 92 692 doctors in the UK (n=13), US (n=4), Norway (n=3), Japan (n=2), Spain (n=1), Canada (n=1). All studies identified personal needs, categorised as work-life balance, emotional regulation, self-perception and identity, and engagement with return process. Seventeen studies highlighted social needs relating to professional culture, personal and professional relationships, and illness stigma. Organisational needs found in 22 studies were flexibility and job control, work design, Occupational Health services and organisational culture. Emerging resources and recommendations were highlighted. Variable quality and high risk of biases in data collection and analysis suggest cautious interpretation. CONCLUSIONS: This review posits a foundational framework of returning doctors' needs, requiring further developed through methodologically robust studies that assess the impact of length and reason for absence, before developing and evaluating tailored interventions. Organisations, training programmes and professional bodies should refine support for returning doctors based on evidence.
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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 | 0.024 | 0.095 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".