The role of medical support workers during the Covid-19 pandemic in the National Health Service in the UK: a qualitative service evaluation at the Oxford University Hospitals Foundation Trust
Bibliographic record
Abstract
Abstract Background During the COVID-19 pandemic, the National Health Service (NHS) England created a short-term position known as a Medical Support Worker (MSW), for International Medical Graduates (IMGs) and qualified doctors who had left medicine, to return to medical practice. We conducted a service evaluation of the MSW role at Oxford University Hospitals NHS Foundation Trust (OUHFT), with the aim of understanding how MSWs were perceived and contributed to the NHS, factors driving MSWs’ career choices, the short- and long-term goals of the position, analysing the perspectives of MSWs, their supervisors and recruiters. Methods A qualitative case study approach was adopted. A total of nine semi-structured interviews and two focus group discussions involving 18 participants were conducted with MSWs, their supervisors and recruiters based in OUHFT. A thematic analysis and narrative synthesis of results were conducted. Results Findings were categorised into micro, meso, and macro levels of the health system. At the micro level, MSWs were identified as a diverse group of highly qualified international medical graduates (IMGs) holding a supernumerary role, who contributed their skills during the pandemic. At the meso level, the importance of a comprehensive induction by the hospital was highlighted by all participants, to clarify the job responsibility and familiarise MSWs with the local health system. At the macro level, the role enabled familiarisation and integration of MSWs within the NHS with the aim of obtaining a license to practice as a doctor in the UK. Conclusions This service evaluation highlighted the importance of the role of MSWs during the pandemic. The MSW scheme could be a pathway for IMGs to integrate into the NHS and fill workforce shortages. This study has the potential to inform the NHS long-term policy on the role of MSWs and the integration of IMGs into the workforce.
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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 | 0.032 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".